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Weather Prediction(analysis) using Soft kind of computing techniques 4

Weather Prediction(analysis) using Soft kind of Computing te...

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4th IEEE international conference on Advance computing and Innovative Technologies in engineering, ICACITE 2024

作者： Revathi, Pogiri Bhavani, Santhoshi Karthika Priya Dharshini, G. Rajaram, Gnanajeyaraman Siddhan, Saravanan Dwibedi, Rajat Kumar Gmr Institute of Technology Department of Ece Andhra Pradesh Rajam India Avanthi Institute of Engineering and Technology Department of Ece Andhra Pradesh Thagarapuvalasa India Saveetha Engineering College Thandalam Saveetha nagar Chennai602105 India Saveetha Institute of Medical and Technical Sciences Simats Saveetha School of Engineering Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Department of Ece Tamil Nadu Chennai India

ISBN: (纸本)9798350360165

Identifying weather conditions is a prevalent issue across all industries. Autonomous vehicles must accurately assess meteorological conditions to adapt their driving behavior. Contemporary farming relies on analyzing present weather patterns. A potential approach could involve implementing a system that detects weather conditions from images. Since no specialized sensors are required, the entire setup should be quite affordable. Transfer learning (TL) enables the development of picture categorization methods with a limited database. This work introduces 3 weather detection systems. The designs are built using InceptionV3, MobileNetV2, & ResNet50 designs. Their effectiveness is evaluated and explained. © 2024 IEEE.

关键词： Neural networks

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Enhanced Loan Approval Prediction System using Ensemble Machine learning Techniques 3

Enhanced Loan Approval Prediction System using Ensemble Mach...

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3rd international conference on Automation, computing and Renewable Systems, ICACRS 2024

作者： Gothai, E. Rajalaxmi, R.R. Thamilselvan, R. Sridhar, K. Department of Computer Technology Kongu Engineering College Tamilnadu Perundurai India Department of Computer Science and Engineering Kongu Engineering College Tamilnadu Perundurai India MSc Software Systems Kongu Engineering College Erode India

ISBN: (纸本)9798331532420

Banks play a pivotal role in generating significant profits through loan operations. However, the challenge lies in accurately identifying genuine loan applicants who are likely to repay their loans. Manual processes are prone to misconceptions, prompting the development of a loan prediction system using deep learning. This system aims to automate the selection of eligible candidates, benefiting both bank staff and applicants by drastically reducing the loan approval timeframe. Two key machine learning algorithms such as Artificial Neural Network (ANN) and Support Vector Machine (SVM) are employed in this work for applicant selection. SVM, a popular supervised learning algorithm, creates a decision boundary or hyperplane to segregate classes in n-dimensional space. Conversely, ANN is inspired by biological neural networks, which mimic the organization of neurons in the input layer, hidden and output layers of the human brain. The integration of ANN and SVM in an ensemble model enhances accuracy compared to traditional methods. This collaborative approach ensures precise predictions of loan repayment likelihood based on applicant data and past loan eligibility records. The model's performance is measured using accuracy. The outcomes of the research were promising and the loan prediction system defines the minimum and maximum loan amounts that can be sanctioned to an applicant. By leveraging the strengths of both ANN and SVM, the ensemble model serves as a robust tool for accurately identifying worthy loan applicants, streamlining the loan approval process, and minimizing the risk of default. The accuracy of the algorithm is improved by this method to 91%. © 2024 IEEE.

关键词： Support vector machines

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5th EAI international conference on Cognitive computing and Cyber Physical Systems, IC4S 2024

5th EAI International Conference on Cognitive Computing and ...

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5th EAI international conference on Cognitive computing and Cyber Physical Systems, IC4S 2024

ISBN: (纸本)9783031770746

The proceedings contain 102 papers. The special focus in this conference is on Cognitive computing and Cyber Physical Systems. The topics include: Comparative Analysis Between Fuzzy Theorem and KNN Methodology for Fault and Anomaly Detection;Production Classification in E-Commerce Based on Product Descriptions with Natural Language Processing (NLP) and Machine learning Models;defense Strategy Security Mechanism for Sensor Networks;an Efficient Approach for Food Demand Forecasting Using an Ensemble Technique and Statistical Analysis;identification of Different Medicinal Plants Using Machine learning and Image Processing;Leveraging the Power of MRMR in Machine learning Models for Multi Class Classification of Rice to Promote Sustainable and Efficient Smart Farming;performance Analysis of Word Recognition System Using Tensor Flow;A Distinct Artificial Feed Forward Neural Network (AF2NN) Model for Predicting Compressive Strength of Geo-Polymer Concrete;Performance Assessment of Deep learning-Models for Kidney Tumor Segmentation using CT Images;investigation of Quantum Machine learning for Smart Eco System Focusing on Energy Optimization;Classification of Skin Cancer Using CNN with Transformer Layer;a Unified Approach to Smart Coconut Farming with IoT and Deep learning for Recommendation of Pesticides and Fertilizers;classifying the Severity of Diabetic Retinopathy Using Deep learning;Exploring Prominent Convolutional Neural Network Frameworks to Identify COVID-19 Deceases by Using Medical Images;non-invasive Technique for Detecting Glycosuria Through Image Processing and Deep learning Approaches;deep learning Strategies for Multiclass Skin Disease Classification;AI-Driven Glaucoma Susceptibility Assessment and Lifestyle Guidance;Deep learning in Cartoon Moderation: Distinguishing Child-Friendly Content with CNN Architectures;THz Wideband Metamaterial Absorber for Different Applications.

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Machine learning Approaches for Efficient Traffic Flow in Smart Cities 3

Machine Learning Approaches for Efficient Traffic Flow in Sm...

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3rd Odisha international conference on Electrical Power engineering, Communication and computing Technology, ODICON 2024

作者： Kulkarni, Aniket Anitha, P. Valluri, John Yesudas Sunena Rose, M.V. Hemavathi, U. Hussein, Omar M. Software Qa Manager Drata Inc San DiegoCA United States Kalasalingam Academy of Research and Education Department of Computer Applications Tamil Nadu Srivilliputhur India Faculty of Management Studies Bengaluru India Department of Computer Science and Engineering Bengaluru India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Cse Avadi Chennai India Al-Farahidi University Medical Technical College Medical Instrumentation Engineering Baghdad Iraq

ISBN: (纸本)9798350354379

These days, a lot of towns struggle with traffic jams during peak hours, which increment contamination, commotion, and feelings of anxiety for the general population. Because neural networks (NN) and machine learning (ML) techniques can handle large numbers of parameters in massive amounts of data and dynamic behaviour over time, they are replacing analytical and statistical methods in the solving of real-world problems. This research presents a convergence of machine-learning (ML) and deep-learning (DL) calculations for traffic stream prediction;this paves the way for flexible traffic signals, which can be implemented through the use of a calculation that adjusts the timing in accordance with the anticipated traffic flow or by controlling traffic signals to some degree. The proposed ML and DL models are constructed, validated, and tested using two publicly accessible datasets. The first depicts the total number of cars subjected to checks using various sensors at six junctions over the course of 56 days. In this research, ML and DL models are created using data from four of the six junctions. The excellent performance metrics obtained by all ML and DL computations suggest that their application to intelligent traffic light management is feasible. © 2024 IEEE.

关键词： Traffic congestion

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Application and Optimization of Intelligent teaching Assistance System in Physical Experiment teaching 24

Application and Optimization of Intelligent Teaching Assista...

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3rd international conference on Artificial Intelligence and Education, ICAIE 2024

作者： Ma, Jinbei Shandong University of Engineering and Vocational Technology Shandong Jinan China

ISBN: (纸本)9798400712692

This paper designs an intelligent teaching assistance system to improve the effect of physical experiment teaching based on artificial intelligence technology. The system integrates intelligent algorithm modules for real-time data processing and analysis to ensure accurate monitoring of the experimental process. The system can automatically collect, process and analyze experimental data, reduce the burden of teachers in data management, and improve the efficiency of experimental teaching. The system was experimentally verified in multiple teaching scenarios, and the results showed that it can effectively assist students to complete experimental operations and improve data accuracy and experimental efficiency. The simulation results include specific and accurate data analysis, which verifies the stability and practicality of the system. The intelligent teaching assistance system proposed in this paper can optimize the experimental teaching process and improve students' learning effects. At the same time, it has good scalability and can be applied to other scientific teaching fields. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Data reduction

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Internet of Things (IoT): Role of Machine learning in Power Management and Optimization using Regression Analysis 5

Internet of Things (IoT): Role of Machine Learning in Power ...

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5th IEEE international conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Kumar, Hemant Mamoria, Pushpa Dewangan, Deepak Kumar Kumari, Shivani Chhatrapati Shahu Ji Maharaj University Department of Computer Science & Engineering Kanpur India ABV-IIITM Department of Computer Science & Engineering Gwalior India Rama University Department of Computer Science & Engineering Kanpur India

ISBN: (纸本)9798350383522

This article discusses many aspects of internet of thing related to implementation of energy efficient devices in IoT based network. As everyone knows, implementations of smart automation is possible due to energy efficient devices of IoT network at different levels in data transmission. The purpose of this research is to predict the energy efficiency with the help of machine learning model. Therefore, for this we have implement linear regression method which is basically a supervised machine learning technique to predict and analyzed the different factors which affects the power consumption on a particular time in IoT network. In this article we have chosen a linear regression method to predict the cumulative power needed for any IoT device during data transmission. After careful observations we have found that using ML model is more suitable for predicting the total power requirement for any IoT device in the network rather than going for direct calculation. Apart from this ML model selection, power consumption issues in IoT devices while in use has been discussed systematically and the impact of using different ML techniques is also being discussed. © 2024 IEEE.

关键词： AI IoT ML NFC RFID UI WSN

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Identifying Critical Success Factor for Effective Adoption of Mobile learning Application: An Empirical Study in Indian Context 12

Identifying Critical Success Factor for Effective Adoption o...

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12th international conference on Cloud computing, Data Science and engineering, Confluence 2022

作者： Wairiya, Manoj Sahu, G.P. Tyagi, Neeraj Mnnit Allahabad Cse Department Prayagraj211004 India Mnnit Allahabad Sms Department Prayagraj211004 India

ISBN: (纸本)9781665437011

The Advent of Advanced Mobile Technology along with supportive strong internet Infrastructure creates an opportunity to educators to use this technology for teaching learning purpose, but still there is hesitancy among educators and students to adopt it for education. In developing countries specially in Indian context M-learning is in its initial stage. The stakeholders involved do not know how to implement M-learning in an efficacious way. In this research critical success factors have been investigated, which may play paramount role in M learning adoption process. To carry out the research data has been collected from students of higher education institutions across the nation by on line mode. The collected data is processed and analyzed using Structured Equation modeling and confirmatory factor analysis. Statistical package SPSS and AMOS has been used to analyze the data. Findings of the research indicated that social influence Facilitating condition, Ubiquity, Self-management of learning, Attitude, Government support are critical factors which significantly affect M-learning adoption. Based on these critical success factors a validated model for effective adoption of M learning has been developed. © 2022 IEEE.

关键词： Students

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Applicability of Machine learning in Waste Water Quality Detection 5

Applicability of Machine Learning in Waste Water Quality Det...

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5th IEEE international conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Shingare, Shyamala Prakash Mandake, Manoj B. Patil, Rahul N. Thokal, Ashwini G. Qureshi, Yaser Thakar, Chetan M. Bharati Vidyapeeth College of Engineering Department of Chemical Engineering Navi Mumbai India Government College Department of Zoology Khertha Balod India Savitribai Phule Pune University Pune India

ISBN: (纸本)9798350383522

The amount of data available in the aquatic environment is unprecedented, making the employment of critical techniques like data analysis, categorization, and prediction necessary. These approaches are necessary for the design, supervision, modeling, evaluation, and improvement various systems for managing and treating water. The machine learning models and findings have grown to be powerful instruments for studying the aquatic environment. Wastewater treatment plants make significant use of a range of sensors to regulate energy usage and ensure high-quality effluent. These sensors generate massive volumes of data that automated systems can efficiently monitor. To cope with this flood of data, several statistical and learning methods have been created to automatically detect problems. Furthermore, reducing water pollution, enhancing water quality, and controlling watershed security have all showed potential for machine learning (ML). We examine the assessment of water quality in several aquatic habitats, including This document discusses groundwater, sewage, drinking water, and saltwater. This article delves deeply into the several uses of machine learning. learning techniques for water quality evaluation. The exponential rise of information in the aquatic setting has made the use of fundamental methods like data analysis, categorization, and prediction indispensable. Machine learning models are becoming essential tools in water environment research, enabling the creation and system management and treatment optimization for water. ML algorithms may be used to automatically identify mistakes and assess water quality in a variety of aquatic environments. © 2024 IEEE.

关键词： Detection etc Machine learning Neural network Waste water

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Intelligent Platform for College English Blended teaching Based on Mobile learning 6th

Intelligent Platform for College English Blended Teaching Ba...

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6th European-Alliance-for-Innovation (EAI) international conference on Advanced Hybrid Information Processing (ADHIP)

作者： Fan, Lin Yantai Vocat Coll Yantai 264670 Peoples R China

ISBN: (纸本)9783031287862;9783031287879

There is a large amount of heterogeneous data in the hybrid teaching platform, and the amount of data is large and various, resulting in low efficiency of database retrieval, which in turn affects the response time of platform query. Design an intelligent platform for college English blended teaching based on mobile learning. The hardware part adopts the combination of FPGA and CPU to realize data collection and processing, and the read-write control module sends data to the process control module to complete the read-write. In the software part, an intelligent platform software architecture is established according to teaching needs, a platform database is designed, and data is stored and accessed through relational databases and SQL. Based on mobile learning, the functional modules of the teaching software are implemented. The client directly faces the user. It receives and calculates and processes the user data, and then sends a request to the server. The test results show that the average response time of the English blended teaching intelligent platform designed in this paper is lower than that of the traditional teaching platform, and the overall cost acceleration is lower than that of other platforms.

关键词： Mobile learning College English Blended teaching Intelligent platform teaching platform

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Enhancement of Level of Security using Wireshark Through Continuous Monitoring and Detection System 4

Enhancement of Level of Security using Wireshark Through Con...

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4th IEEE international conference on Advance computing and Innovative Technologies in engineering, ICACITE 2024

作者： Bhat, C.Rohith Mane, Sulakshana Bhausaheb Bhatt, Chandradeep Verma, Garima Naser, Sarmad Jaafar Jweeg, Muhsin Department of Computer Science and Engineering India Bharati Vidyapeeth College of Engineering Department of Computer Engineering Navi Mumbai India Graphic Era Hill University Computer Science and Engineering Dehardun248002 India Graphic Era Deemed to Be University Computer Science& Engineering Uttarakhand Dehradun248002 India National University of Science and Technology Collage of Nursing Dhi Qar64001 Iraq Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9798350360165

We suggest using edge computing to install an intrusion detection system (IDS) at the network edge in order to address the problem of intrusion detection in wireless sensor networks (WSNs). This IDS functions as a proactive defence system designed specifically with WSNs in mind. Our method combines an arithmetic optimisation algorithm (AOA) with Wireshark and machine learning to create an edge intelligence framework for identifying denial-of-service (DoS) assaults in wireless sensor networks (WSNs). We use the wireshark for optimisation adjustment and a parallel strategy to promote communication between populations in order to increase model accuracy. Our model achieves 98.9% accuracy (ACC) through simulated studies utilising the IDS dataset in Matlab2018b. The effectiveness and practical significance of our suggested intrusion detection approach are highlighted by these experimental results. © 2024 IEEE.

关键词： Network intrusion

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