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检索条件"任意字段=3rd International Conference on Machine Learning and Computer Application, ICMLCA 2022"

共 1897 条记录，以下是641-650 订阅

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3rd international conference on Next Generation Arithmetic, CoNGA 2022

3rd International Conference on Next Generation Arithmetic, ...

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3rd international conference on Next Generation Arithmetic, CoNGA 2022

ISBN: (纸本)9783031097782

The proceedings contain 8 papers. The special focus in this conference is on Next Generation Arithmetic. The topics include: A Posit8 Decompression Operator for Deep Neural Network Inference;qtorch+: Next Generation Arithmetic for Pytorch machine learning;ACTION: Automated Hardware-Software Codesign Framework for Low-precision Numerical Format SelecTION in TinyML;multiPosits: Universal Coding of Rn;comparing Different Decodings for Posit Arithmetic;universal: Reliable, Reproducible, and Energy-Efficient Numerics.

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Long-Term Energy Demand Forecasting in Thailand with Ensemble Prediction Model 17

Long-Term Energy Demand Forecasting in Thailand with Ensembl...

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17th international Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP) / 3rd international conference on Artificial Intelligence and Internet of Things (AIoT)

作者： Chatunapalak, Isariyanatre Kongprawechnon, Waree Kudtongngam, Jasada Thammasat Univ Sirindhorm Int Inst Technol Pathum Thani Thailand Minist Higher Educ Sci Res & Innovat Natl Sci & Technol Dev Agcy Natl Elect & Comp Technol Ctr NECTEC Pathum Thani Thailand

ISBN: (数字)9781665457279

ISBN: (纸本)9781665457279

This research has proposed to utilize the combination of machine learning models (ML models) to optimally forecast the energy demand in Thailand. The various ML models are explored in which the individual and the combination of ML models are each optimized and evaluated for their best achievable performances. Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) are utilized to compare models' performances. A total of 4 ML models are executed, which include Artificial Neural Network (ANN), Decision Tree (DT), Random Forest (RF) Ensemble and proposed Vote Ensemble models. The results show that, by means of ensemble or model combination, the Vote Ensemble model could perform well with the lowest RMSE for training and testing of 613.63 and 666.52 and the lowest MAPE of 3.59% accordingly while also using less execution time of 3 minutes and 56 seconds.

关键词： Ensemble learning LTLF Forecasting Energy Demand machine learning

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Intrusion Detection System in IoT Network by using Metaheuristic Algorithm with machine learning Dimensional Reduction Technique 3

Intrusion Detection System in IoT Network by using Metaheuri...

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3rd international conference on Computing, Analytics and Networks, ICAN 2022

作者： Anusha, Chintam Sravani, A. Anusha, Jetti Lakshmi, Choudari Kumari, Gantla Santoshi Vignan's Institute of Information Technology Computer Science and Engineering Visakhapatnam India GITAM School of Technology Computer Science and Engineering Visakhapatnam India

ISBN: (纸本)9781665499446

The network security is a main problem in any disseminated framework. To providing the safe and secure network we have been proposed anomaly detection system against from the suspicious attacks. The essential goal of this exploration is to plan effective IDS for IoT organization. The interruption discovery assumes a fundamental part in recognizing various assaults on IoT and upgrades the IoT performance. In this paper, one of the metaheuristic algorithms GSO (glow-worm swarm optimization) algorithm add with machine learning technique PCA (principal component analysis) used for providing the IDS. Therefore, the metaheuristic-based intrusion discovery model to recognize malicious activities by utilizing the NSL-KDD dataset. Dimensional reduction technique Principal component analysis is utilized for extraction of features and GSO algorithm is utilized to classify the various class of malicious attacks in the dataset. We have considered some metrics like precision, detection rate, recall, accuracy and FAR are computed. The suggested method evaluated better in each parameter compared with other existing techniques. © 2022 IEEE.

关键词： Principal component analysis

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A Novel Fake News Detection Approach Using machine learning 3

A Novel Fake News Detection Approach Using Machine Learning

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3rd international conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Mahijith, K.V. Unni Sunil, Aswin Ashin, C. Sreelathan, Abhinand Thomas, Manoj V. Vimal Jyothi Engineering College Department of Computer Science and Engineering Kerala Kannur670632 India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

The largest number of smartphone users choose to check statistics through social networks over the Internet. Social media websites post facts and provide certifications. This question may relate to the credibility of knowledge and articles distributed on social networks such as WhatsApp, Facebook pages, Twitter, and various microblogs and social networking *** is very dangerous for society to simply accept rumors and false information as truth. An hour's need is to prevent gossip, especially within developing countries such as India, and to prevent interest in relevant and credible news *** article will show you the versions and how to detect fake news accordingly. We used machine learning and natural language processing to gather news and tried to use Vector's support engine to determine if the news was real or fake. The proposed options is compared with the existing model. The proposed version works correctly and determines accuracy with up to 93.6% accuracy. © 2022 IEEE.

关键词： Support vector machines

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Research on the application of data mining technology in software engineering 3

Research on the application of data mining technology in sof...

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3rd international conference on machine learning and Big Data Analytics for IoT Security and Privacy, SPIoT2023

作者： Yitao, Lin School of Software Heilongjiang University Heilongjiang Province Harbin450000 China

The role of data mining in software engineering is obvious, but there is a lack of mining depth. In the past, engineering layout and function distribution problems in software development, and the framework construction was unreasonable. Therefore, this paper proposes a software system based on data mining technology. First, the software engineering project is divided, and the software engineering project design is carried out according to the interactive requirements to realize the preprocessing of software engineering. Then, according to the design criteria, the design collection of software engineering is formed, and the information system is analyzed in depth. MATLAB simulation shows that the design interactivity and accuracy of data mining are superior to traditional design methods under the condition that software engineering requirements are consistent. © 2023 Elsevier B.V.. All rights reserved.

关键词： Data mining

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machine learning-Based Path Loss Estimation Model for a 2.4 GHz ZigBee Network 3rd

Machine Learning-Based Path Loss Estimation Model for a 2.4 ...

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3rd international conference on computer Vision, High Performance Computing, Smart Devices, and Networks, CHSN 2022

作者： Ragam, Prashanth Karthik, Guntha Jagadesh, B.N. Jyothi, Sankati School of Computer Science and Engineering VIT-AP University Inavolu Beside AP Secretariat Andharpradesh Amaravathi India Department of ECE Stanely College of Engineering and Technology for Women Telangana Hyderabad India Software Development Cell VIT-AP University Inavolu Beside AP Secretariat Andharpradesh Amaravathi India

ISBN: (纸本)9789819966899

Wireless sensor networks (WSNs) and Internet of Things (IoT) have received remarkable attention from the past few years in various applications. Modeling of path loss (PL) for the deployment of a developed WSN system is a crucial task owing to the time-consuming and elegant operation. However, radiofrequency (RF) engineers adopted either deterministic or stochastic empirical models to estimate the PL. In general, empirical models utilize predefined influenced parameters including path loss (dB), path loss exponent (ϒ), and other significant parameters. Although, empirical models differ significantly from original measurement due to consideration of different terrains. In this study, an endeavor has been made to develop a machine learning-based model to estimate the path loss for a standard ZigBee communication network operating on a 2.4 GHz carrier frequency deployed in an urban area. An experimental setup was designed and tested in line-of-sight (LOS) and non-line-of-sight (NLOS) conditions to collect the influence parameters such as received signal strength indicator (RSSI), frequency, distance, and transmitter antenna gain. Besides that, environmental parameters such as temperature and humidity are also included. In this context, a three-layer, feed-forward backpropagation multilayer perception neural network (BPNN) machine learning and Log-Distance empirical models were employed to estimate the PL. The obtained results reveal that the BPNN model noticeably enhanced the coefficient of determination (R2) and reduced root mean square error (RMSE) compared with the empirical model. The R2 and RMSE metrics were obtained as 0.97220 and 0.03630 in the NLOS scenario as well as 0.99820 and 0.00773 in the LOS environment, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Wireless sensor networks

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Design and Implementation of Text Classifier Based on Convolutional Neural Network 3

Design and Implementation of Text Classifier Based on Convol...

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3rd international conference on computer Science and Management Technology, ICCSMT 2022

作者： Xie, Nana Chongqing Aerospace Polytechnic Chongqing China

ISBN: (纸本)9798350320404

With the continuous development and application of artificial intelligence technology, text classification gradually changes from shallow learning mode to deep learning mode. Deep learning mode has obvious improvement in process design, model construction and accuracy of classification prediction results. Therefore, this paper takes text classification as the research object, based on the deep learning algorithm, builds a text classifier by using TextCNN Convolutional Neural Network, and trains the corresponding model with TensorFlow deep learning framework and Keras advanced neural network API in Python environment, so as to form an intelligent text classifier that can be called by Web Server. In addition, *** framework and C# language will be selected to complete the development of Web Server, improve the definition and deployment of functions and data interfaces, and generate a Web-based text classification system. The overall design of the system conforms to the B/S architecture standard, which supports users to complete the classification results of the input text content by simple operation of the client interface. After testing, the text classifier based on TextCNN has the ability to quickly extract local features, and it is more suitable for the classification prediction of long-sequence text content, and its execution efficiency and classification accuracy are obviously improved. © 2022 IEEE.

关键词： Deep learning

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Ensemble learning on a Weak Correlated Android Malware data using Stratified K-Fold 3

Ensemble Learning on a Weak Correlated Android Malware data ...

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3rd IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2022

作者： Soundrapandian, Pradeepkumar Duraisamy Geetha, S. VIT University Chennai India

ISBN: (纸本)9781665462006

In Android apps communicates with other apps by using Intent or PendingIntent. An Intent enables Android applications to share information between apps (like data, action, etc.,), and the PendingIntent delegate's authority to other apps to perform the required action in the future. Android supports apps to collaborate with any $3^{\mathrm{rd}}$ party apps using a flexible communication model called Implicit Intent-based Communication. Though this communication channel is effective in collaboration it is unprotected and unsafe by default. Any application (even malware) can register to this implicit channel, and thereby can sniff the intents exchanged through the channel, making it vulnerable to malware attacks. In case, if an app is exchanging its sensitive data like GPS location or exchanging PendingIntent using implicit intents, in turn, this leads to unauthorized access and privilege escalation attacks. In this paper, we leverage the machine-learning techniques for security predictions in order to identify such possible threats from the apps' binary inspection, and thereby our research can assist cyber forensic tools to identify the vulnerabilities caused by dynamic characteristics present in an application before executing the application itself. This paper presents a statistical model to analyze the malware nature of a mobile application: (1) based on the PendingIntent Flag usages, and (2) based on the type of Broadcast across apps. Our app classification achieved an F-score of 78.7%. © 2022 IEEE.

关键词： learning systems

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An Approach to Criminal Suspect Prediction Software using machine learning Classifiers 3

An Approach to Criminal Suspect Prediction Software using Ma...

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3rd IEEE international conference on Computing, Communication, and Intelligent Systems, ICCCIS 2022

作者： Arya, Ashima Sehgal, Mitu Bhatia, Neha Aggarwal, Nitisha Khurana, Shiraz Kiet Group of Institutions Department of Csit Delhi-NCR Ghaziabad India Panipat Institute of Engineering and Technology Department of Information Technology Samalkha India Sharda University Department of Computer Science and Engineering Noida India

ISBN: (纸本)9781665462006

The goal and the objective of this project is to predict the suspect of criminal and its location by analyzing criminal records and finding criminal hotspots. Investigations in India regarding larceny are very futile in nature and seldom result in apprehension of the criminal. Our software is for police officials to lead their investigation effectively and work towards capturing the unrighteous using data analytics thereby uncovering theft and criminal trends. Such trends lead to uncovering dimensions for an investigation which are difficult to recognize through manual inspection techniques. Such software is not only helpful for the police for effective and smart work in case of low-profile crimes but also the increase in arrests for larceny would ultimately lead to low crime rate and a lawful society. The inclusion of machine learning technique such as KNN technique is used to suspect the criminal by analyzing its data. Through the use of such software for police work in the hope of reduction in crime rate is a progressive step forward for Digital India. © 2022 IEEE.

关键词： Crime

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ANN and Deep learning Classifiers for BCI applications 3

ANN and Deep Learning Classifiers for BCI applications

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3rd international conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022

作者： Aswin, K.S. Purushothaman, Manav Kotilingeswaragupta, Polisetty Sritharani Angel, T.S. Department of Electrical and Electronics Engineering Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9781665410052

ISBN: (纸本)9781665410052

The Brain computer interface (BCI) or neural control interface is a technology that allows humans to control a computer or other computing devices on the basis of information inferred from thoughts. The communication is between a wired brain and an external device. The study comprises the acquisition of EEG signals and its classification. The classification process of EEG signals includes signal detection, feature extraction and classification of brain waves. The classification works on the principle of finding the best hyper-plane that separates the two classes in input space. In the proposed work, the application of brain waves for the direction control of a wheelchair in forward and backward direction is studied. Developed artificial neural network and deep neural network for the classification of EEG signals, and compared their performances using precision, recall and f1-score. Also a website application was developed for the advanced prediction and control. © 2022 IEEE.

关键词： Electroencephalography

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