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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Sriram, K.P. Anbalagan, E. Sasikumar, S. Kumar, M. Guru Vimal Paramesh, J. Sujatha, P. Kola Assistant Professor Information technology St.Joseph's Institute of Technology OMR Chennai – 600119 Professor Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Professor Department of Computer Science and Engineering Saveetha Engineering College Chennai Associate Professor Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Professor Department of Computer Science and Engineering Mohamed Sathak A.J. College of Engineering Ekattur OMR Siruseri Chennai Associate Professor Department of Information Technology MIT Campus Anna University Chennai Tamil Nadu India – 600044

ISBN: (纸本)9798350383867

This research paper is about the effectiveness of a fusion of machine learning and artificial intelligence into smart irrigation systems in terms of both safety and benefit of the environment and the economy in farming. We've experienced a intensive process of experiment and application of this succeeded method in a real world scenario and we've also detailed the performance of the dynamic irrigation scheduling and the adapter sensor network optimization. Results indicate a significant improvement in key metrics: scheduling was done dynamically which on average was a 10% rise in crop yield and 15% water saving, on the other hand, sensor network optimization with adaptive technology resulted into a 15% increase in cover area and 20% improvement achieved in data transmission reliability. Furthermore, the introduction of the smart irrigation system got impressive results signified by a 10 kg more production per hectare, irrigation efficiency that rose from 70% to 85%, and saving 20% of the water consumed. This calls to mind the capability of cutting-edge technologies to contribute to innovative water management paradigms, improved resource use, and security in food production in face of unpredictable climatic variability. ©2024 IEEE.

关键词： Adversarial machine learning

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Advancing Personalized Recommendation Systems with a Groundbreaking Collaborative Filtering Algorithm Driven by Machine Learning 2

Advancing Personalized Recommendation Systems with a Groundb...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Anbalagan, E. Sasikumar, S. Kumar, M. Guru Vimal Paramesh, J. Sriram, K.P. Professor Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Professor Department of Computer Science and Engineering Saveetha Engineering College Chennai Associate Professor Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Professor Department of Computer Science and Engineering Mohamed Sathak A.J. College of Engineering Ekattu Siruseri Chennai India Assistant Professor Information Technology St.Joseph's Institute of Technology Chennai India

ISBN: (纸本)9798350383867

By introducing this collaborative filtering algorithm, which is dependent on machine learning that can be used in enhancing the user-based recommendation systems, this paper is trying to achieve more advanced personalized recommendation systems. Utilizing recent innovative designs of networks together with relevant contextual information, the algorithm provides high accuracy recommendation, scalability, and aids in improving user experience than traditional approaches. The model is shown to perform exceedingly well in all respects by its being subjected to a thorough assessment and compared with the baseline and enhanced models. It joins ranks with the best in all the indices of performance like precision, recall, F1-score, mean average precision and user satisfaction. In addition, hyperparameter tuning, and scalability metrics, so ending efficiency for model configuring and deployment recommendation models which could deal with large-scale datasets and offer users timely suggestions are of vital importance. To summarize, these results highlight the disruptive character of the latest machine learning algorithms that might create a new reality of recommendation systems with the trend of the time of individualization of relationships in the digital world. ©2024 IEEE.

关键词： Collaborative filtering

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A Study of Deep Learning Techniques for Predicting Breast Cancer Types

A Study of Deep Learning Techniques for Predicting Breast Ca...

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International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2022

作者： Ashwini, P. Suguna, N. Vadivelan, N. Research Scholar Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Chidambaram India Associate Professor Department of Computer Science and Engineering Faculty of Engineering and Technology Annamalai University Chidambaram India Professor Department of Computer Science and Engineering Teegala Krishna Reddy Engineering College Hyderabad India

ISBN: (纸本)9789819947164

Breast cancer is today’s deadly health issue which causes high mortality in woman worldwide. The preliminary detection and classification may help for proper treatment of the same. Understanding the causes of this cancer using traditional machine learning techniques misread feature extraction are leading to a complication. Traditional machine learning models are best suited for feature-based raw data. The novel deep learning techniques have been introduced for diagnosis of breast abnormalities using various imaging modalities like mammogram, magnetic resonance imaging (MRI) and ultrasound with high accuracy. This survey focuses on challenges of classical machine learning models and supervene efficient prediction models using novel deep learning techniques. This review presents comparison analysis of traditional machine learning and deep learning models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Magnetic resonance imaging

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A Novel Ensemble Methodology to Validate Fuzzy Clusters of Big Data 4th

A Novel Ensemble Methodology to Validate Fuzzy Clusters of B...

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4th International Conference on Trends in Cognitive Computation engineering, TCCE 2022

作者： Sardar, Tanvir Habib Sarkar, Rashel Ahmed, Sheik Jamil Bandyopadhyay, Anjan Assistant Professor Department of CSE GITAM School of Technology GITAM University Bangalore India Associate Professor Department of Computer Science University of Science and Technology Meghalaya Ri-Bhoi India Assistant Professor School of Computer Science and Engineering & Information Science Presidency University Bangalore India Assistant Professor Kalinga Institute of Industrial Technology Odisha Bhubaneswar India

ISBN: (纸本)9789811994821

The clustering of datasets is a widely used technique in unsupervised machine learning. The cluster quality evaluation is a tricky problem because external validation is usually not possible for clustering. This happens due to the unavailability of external proof. Although there are many methods developed and experimented with to validate the results obtained from clustering, it is always preferred to use more than a few cluster validity measures. The big data usually contain a high percentage of noise in it, making it necessary to use some additional techniques to incorporate while clustering big data. In this work, we have clustered document big data using fuzzy logic-based enhancement of traditional K-Means and K-Medoids. This work has suggested the Ensembling of seven different cluster quality validity measures to determine the best quality of fuzzy clusters. The Reuters standard document dataset is clustered using different cluster numbers, and the proposed ensemble methodology is proven to determine optimal numbers of clusters. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

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A Machine Learning-based Approach to Detect Polluting Vehicles in Smart Cities 6

A Machine Learning-based Approach to Detect Polluting Vehicl...

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6th International Conference on Smart Cities, Internet of Things and Applications, SCIoT 2022

作者： Afshar, Bahar Fathy, Mahmood Asgari, Maryam Shahverdy, Mohammad Shahverdi, Pooya Islamic Azad University Professor Hessabi Branch Department of Computer Engineering Tafresh Iran Iran University of Science and Technology Department of Computer Engineering Tehran Iran

ISBN: (数字)9781665488914

ISBN: (纸本)9781665488914

After the increase of pollution caused by vehicle congestion, new approaches to reduce pollution have emerged. Although regular visits to technical inspection centers and car repair center can be helpful, a new approach is to achieve effective integration between mobile applications and vehicles. This integration can be achieved using the ELM327 interface, which provides data such as speed, fuel consumption, gas emission, and system failure using the wireless interface to the mobile phone. Nowadays, vehicles have to go to technical inspection centers for pollution testing, which is costly in terms of time and price. This paper presents a machine learning-based method that uses data extracted from vehicle sensors and can determine the amount of pollution emitted from vehicle then warns the driver. Experimental results confirm that the proposed method can efficiently detect Polluting Vehicles. © 2022 IEEE.

关键词： Intelligent systems

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Blockchain-based Secure UAV-assisted Battlefield Operation underlying 5G 14

Blockchain-based Secure UAV-assisted Battlefield Operation u...

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14th International Conference on Information and Knowledge technology, IKT 2023

作者： Pancholi, Dhruvi Jadav, Nilesh Kumar Tanwar, Sudeep Garg, Deepak Zanjani, S. Mohammadali Institute of Technology Nirma University Department of Computer Science and Engineering Ahmedabad 382481 India SR University Distinguished Professor School of Computer Science and Artificial Intelligence Warangal 506371 India Islamic Azad University Najafabad Branch Department of Electrical Engineering Najafabad Iran

ISBN: (纸本)9798350349412

The use of unmanned aerial vehicles (UAVs) has significantly increased during the past few years. UAVs have a wide range of applications, but their employment in the defense sector has grown significantly in recent years as a result of their exceptional effectiveness in reducing casualties and enabling the accomplishment of high-profile as well as time-sensitive operations. UAVs are helpful for numerous defense-related operations, such as border patrol, surveillance, emergency management, damage assessment, delivery of medical supplies and armaments, and many others. However, because wireless communication is open, UAVs can be used to undermine the effectiveness of military operations. In this paper, we developed a blockchain-based framework to mitigate security threats from UAVs operating in a combat scenario. The MATLAB virtual environment is used to simulate a battlefield scenario, including UAVs, sensors, enemies, and friendly nodes. The UAV scenario also includes an enemy that continuously manipulates the UAVs to endanger battlefield operations. The suggested framework employs machine learning algorithms to find malicious UAVs. Further, the proposed framework uses blockchain technology to increase security and lessen data tampering attempts. A smart contract (for data validation) is deployed on an IPFS-based public blockchain. In addition, 5G technology is used, which makes it possible for UAVs and the control station to communicate effectively with lower packet error rates and latency. The proposed framework is evaluated by considering evaluation metrics, such as 5G packet error rate, latency, and transaction and execution costs for the blockchain. © 2023 IEEE.

关键词： Blockchain

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Prediction of the Impact of Covid-19 Vaccine on Public Health Using Twitter

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2022年第1期49卷 19-29页

作者： Syed, Ayesha Ayub Gaol, Ford Lumban Suparta, Wayan Abdurachman, Edi Trisetyarso, Agung Matsuo, Tukoro Ph.D. student at the Department of Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Associate Professor at Informatics Engineering and Information System at Bina Nusantara University Jakarta Indonesia Malaysia Associate Professor at the Department of Doctor of Research in Management at Bina Nusantara University Jakarta Indonesia Assistant Professor at the Department of Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Professor at the Graduate School of Industrial Technology Advanced Institute of Industrial Technology Japan and Department of M-Commerce and Multimedia Applications Asia University Taiwan

Social media is a source of big data. Media like Twitter and Facebook has been used for collecting and analyzing user data for different purposes. The data can be used to analyze people opinions towards certain topics and incidents by applying sentiment analysis and then certain useful insights can be drawn from the analyzed data. During the current time of Covid-19, people have been sharing information regarding Covid-19 statistics, vaccines, and discussing the effects of the vaccine concerning public health. The purpose of this study is to analyze tweet data regarding the Covid-19 vaccine by applying sentiment analysis and predicting the impact of the vaccine on public health. Also, the tweets are analyzed for hidden topics by applying Topic Modelling using Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA). The source of data for this study is Twitter API. The coding and data analysis is done using Python programming language in the Spyder (Scientific Python Development Environment) that is an integrated development environment for scientific programming, testing, and data analysis. The results of the study indicate a greater positive sentiment reflecting a healthy public discussion about the Covid-19 vaccine, information, awareness, and public acceptance. With these results, a positive impact of the Covid-19 vaccine on public health is predicted. The results of topic modeling discovered 10 hidden topics from the tweet dataset. © 2022. All Rights Reserved.

关键词： Vaccines

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A new algorithm for detection of nodes failures and enhancement of network coverage and energy usage in wireless sensor networks

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Materials Today: Proceedings 2023年 80卷 1717-1722页

作者： Satyanarayana, P. Mahalakshmi, T. Sivakami, R. Alahmari, Saad Ali Rajeyyagari, Sivaram Asadi, Srinivasulu ECE V. R. Siddhartha Engineering College Vijayawada India Department of ECE Prasad V Potluri Siddhartha Institute of Technology India Associate Professor CSE Sona College of Technology TN Salem 5 India Department of Computer Science Shaqra University Saudi Arabia Data Analytics Research Laboratory BlueCrest University College Monrovia Liberia

Present natural lives shows rising attention in various applications of the wireless sensor networks (WSNs). Significant applications are isolated and stiff regions where human involvement is hazardous or unfeasible. Some examples are space investigation, battlefield observation, costal, and edge security;nowadays, such networks are needful in several manufacturing and customer applications. A WSN is a device system, indicated as nodes;these nodes intellect the atmosphere and transfer the details collected from the ground through a wireless connection. Statistics are sent, probably via many hops, to a target that can use it nearby or is linked to various networks using the gateway. The sensor nodes might be stationary or changeable. Every node has clear data about the neighborhood or not. Communication over WSN over ecological hazards is a key issue. These constraints might impact the performance of the sensors/direction finding protocols and resource utilization;thus, they may show the way to the node crash situation, i.e., software/hardware breakdown, safety threats, extreme power utilization, etc. It is essential to examine the collision of breakdown over system performance. In the projected method, relay nodes are used as secure nodes for the positioned sensor nodes. The projected method is separated into two phases: the intra segregation stage and inter segregation stage. In the intra segregation stage, the unnecessary nodes reach nearer to the segregation border, and dispersion slowly enlarges the coverage. The projected model is examined through simulations and compares the results with existing methods. © 2021

关键词： Sensor nodes

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Free vibration analysis of functionally graded porous curved nanobeams on elastic foundation in hygro-thermo-magnetic environment

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Frontiers of Structural and Civil engineering 2023年第4期17卷 584-605页

作者： Quoc-Hoa PHAM Parviz MALEKZADEH Van Ke TRAN Trung NGUYEN-THOI Faculty of Engineering and Technology Nguyen Tat Thanh UniversityHo Chi Minh City 700000Vietnam Department of Mechanical Engineering Persian Gulf UniversityBushehr ***Iran Faculty of Mechanical Engineering Le Quy Don Technical UniversityHanoi 100000Vietnam Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh City 700000Vietnam Faculty of Mechanical-Electrical and Computer Engineering School of TechnologyVan Lang UniversityHo Chi Minh City 700000Vietnam Bualuang ASEAN Chair Professor Thammasat School of EngineeringThammasat UniversityPathumtani 12120Thailand

Herein,a two-node beam element enriched based on the Lagrange and Hermite interpolation function is proposed to solve the governing equation of a functionally graded porous(FGP)curved nanobeam on an elastic foundation in a hygro–thermo–magnetic *** material properties of curved nanobeams change continuously along the thickness via a power-law distribution,and the porosity distributions are described by an uneven porosity *** effects of magnetic fields,temperature,and moisture on the curved nanobeam are assumed to result in axial loads and not affect the mechanical properties of the *** equilibrium equations of the curved nanobeam are derived using Hamilton’s principle based on various beam theories,including the classical theory,first-order shear deformation theory,and higher-order shear deformation theory,and the nonlocal elasticity *** accuracy of the proposed method is verified by comparing the results obtained with those of previous reliable ***,the effects of different parameters on the free vibration behavior of the FGP curved nanobeams are investigated comprehensively.

关键词： functionally graded porous material curved nanobeam hygro-thermo-magnetic enriched finite element method

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A Deep Neural Network for Image Classification Using Mixed Analog and Digital Infrastructure 3rd

A Deep Neural Network for Image Classification Using Mixed A...

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3rd International Conference on Emergent Converging Technologies and Biomedical Systems, ETBS 2023

作者： Kala, R. Punitha, M. Poomani Alias Banupriya, P.G. Veerasamy, B. Bharathi, B. Alzubi, Jafar Ahmad Abed Associate professor Department of Computer Science and Engineering Karpagam College of Engineering Tamil Nadu Coimbatore641032 India Department of Information Technology Sethu Institute of Technology Kariapatti Puloor India Department of Computer Science and Engineering Karpagam Institute of Technology Tamil Nadu Coimbatore641032 India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Tamil Nadu Krishnankoil626126 India Department of Information Technology Coimbatore Institute of Engineering and Technology Tamil Nadu Coimbatore641109 India School of Engineering Al-Balqa Applied University Salt Jordan

ISBN: (纸本)9789819986453

Recently, Deep Learning (DL) methods are a well-known solution for image and object detection in computer vision applications. The software-based implementation of DL algorithms requires huge resources and consumes more power. To overcome these challenges, a hardware implementation of Deep Neural Network (DNN) using the integrated analog–digital architecture was proposed in this article. The Very Large-Scale Integration (VLSI)-based DNN implementation utilizes Pulse-Width Modulation (PWM), and analog–digital circuits. The integration of analog and digital architecture integrates the benefits of both circuits, which reduces power consumption and enhances processing speed and classification accuracy. The fabrication of the VLSI chip includes the DNN neuron circuit, PWM circuit, digital adders-subtracters, and SRAM memory. The experimental result verifies that the presented DNN VLSI architecture reduces the power consumption of the neural system, and enhances the image classification performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep neural networks

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