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5th International Conference on Energy, Power, and Environment, ICEPE 2023

作者： Arora, Shivam Prakash, Chandra Rana, Prashant Singh Sood, Kartik Thapar Institute of Engineering and Technology Department of Computer Science & Engineering Punjab Patiala India

ISBN: (纸本)9798350313123

Automatic License Plate Recognition (ALPR) is a technology that allows for the automatic detection and recognition of license plates on vehicles. Deep learning models such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have been applied to enhance license plate detection and character recognition capabilities, improving performance in situations involving distorted or skewed license plates. This paper compares the accuracy of various object detection techniques for License Plate Detection step in ALPR, and their results and performance are analyzed. These object detection techniques include traditional canny edge and contour segmentation, YOLOv5, Single Shot Detector (SSD), FasterRCNN, and RetinaNet. After comparison, it is found that YOLOv5 performs the best in accuracy and inference speed out of all the above-stated techniques. © 2023 IEEE.

关键词： Object detection

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A Study of the Unbalanced Image Dataset for Classification Problem 12

A Study of the Unbalanced Image Dataset for Classification P...

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12th International Conference on System Modeling and Advancement in Research Trends, SMART 2023

作者： Sharma, Davesh Kumar Punhani, Akash Srm Institute of Science and Technology Department of Computer Science and Engineering Modinagar Ghaziabad India

ISBN: (纸本)9798350369861

In the case of experimental data it is evident that the dataset obtained is unbalanced. The main reason for the same is the probability of the occurrence of a particular event in real life. As these events have very less probabilities hence the collection of such sample data is very difficult. Therefore, various approaches have been suggested to overcome this problem. This paper compares the two approaches for handling the unbalanced data. In the first approach, the minimum class with random selection was used to balance the data and in the second approach, the maximum class with random duplicate is used to balance the data. Based on the results of the sugarcane disease detection using densenet with SVM it was evident that incorporating the maximum data available is helping the machine learning model to more effectively. © 2023 IEEE.

关键词： Classification (of information)

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Enhancing Patient Well-Being in Healthcare Through the Integration of IoT and Neural Network

Enhancing Patient Well-Being in Healthcare Through the Integ...

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2024 International Conference on Emerging Innovations and Advanced Computing, INNOCOMP 2024

作者： Singh, Jagendra Shelke, Nitin Arvind Upreti, Kamal Divakaran, Prakash Lingareddy, Nagulapalli Deepika, Sheri School of Computer Science Engineering & Technology Bennett University Greater Noida India Department of Computer Science Ghaziabad India Himalayan University Itanagar India Vignan Institute of Technology and Science Department of CSE Deshmukhi India Sreyas Institute of Engineering and Technology Department of CSE Hyderabad India

ISBN: (纸本)9798350376470

This study analyses the revolutionary integration of Internet of Things (IoT) structures in healthcare through a complete examination of outstanding case research. The first case study focuses on real-time patient fitness monitoring in a clinic setting. The suggested device utilizes an Internet of Things-ready device that has many sensors, including oxygen, pressure, and temperature sensors. The issues of forecasting patient health in advance are handled with the deployment of machine learning models, notably Artificial Neural Networks (ANN), Decision Trees (DT), and Support Vector Machines (SVM). The second case study analyses IoT's effect on patient-precise medication identification and remote fitness monitoring, uncovering issues associated with accessibility, pricing, and human interfaces. Proposed alternatives, which incorporates greater education, increased accessibility, and user-pleasant interfaces with robust technical assistance, have been evaluated with 30 patients over a three-month duration. The results reveal a great growth in impacted person health, along with heightened attention of periodic health monitoring. The results highlight how IoT technologies may transform healthcare procedures by offering pro-active solutions for patients' well-being. This study offers insightful information that may be used to solve practical issues, promote patient-centered solutions, and broaden the scope of the healthcare period. A significant step towards a patient-centered and technologically advanced healthcare environment, the successful outcomes validate the capacity for sustained innovation, cooperation, and improvement in the integration of IoT systems for optimal patient care. © 2024 IEEE.

关键词： Support vector machines

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Enhanced Route Optimization for Wireless Networks Using Meta-Heuristic engineering

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computer Systems science & engineering 2022年第10期43卷 17-26页

作者： S.Navaneetha Krishnan P.Sundara Vadivel D.Yuvaraj T.Satyanarayana Murthy Sree Jagadeesh Malla S.Nachiyappan S.Shanmuga Priya Department of Electronics and Communication Engineering SACS MAVMM Engineering CollegeMaduraiIndia Department of Computer Science and Engineering SACS MAVMM Engineering CollegeMaduraiIndia Department of Computer Science Cihan University-DuhokKurdistan RegionIraq Department of Computer Science and Engineering Bapatla Engineering College BapatlaAndhra PradeshIndia Department of Computer Application National Institute of TechnologyThiruchirapalliIndia School of Computer Science and Engineering Vellore Institute of TechnologyChennaiIndia Department of Computer Science and Engineering M.I.E.T Engineering CollegeTrichyIndia

Wireless Sensor Networks(WSN)are commonly used to observe and monitor precise *** consist of a large number of inexpensive sensor nodes that have been separated and distributed in different *** base station received the amount of data collected by the numerous *** current developments designate that the attentFgion in applications of WSNs has been increased and extended to a very large *** Trust-Based Adaptive Acknowledgement(TRAACK)Intrusion-Detection System for Wireless Sensor Networks(WSN)is described based on the number of active positive deliveries and The Kalman filter used in Modified Particle Swarm Optimization(MPSO)has been proposed to predict knot *** were run for non-malicious networks(0%malicious)and different percentages of malicious nodes were *** findings suggest that the proposed method TRAACK Modified Particle Swarm Optimization(MPSO)packet delivery rate outperforms TRAACKPSO by 3.3%with 0%malicious ***,the packet delivery rate of TRAACKMPSO is 30%malicious,3.5%better than TRAACKPSO in WSN.

关键词： Trust-based adaptive acknowledgment(TRAACK) modified particle swarm optimization(MPSO) intrusion detection system(IDS) kalman filter

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Assessing Lifespan of Lithium-Ion Rechargeable Batteries through Hybrid CNN-LSTM-DNN Method

Assessing Lifespan of Lithium-Ion Rechargeable Batteries thr...

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2023 International Conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： Mary, S. Prince Anto Praveena, M.D. Albert Mayan, J. Nandini, D Usha Bama Krishna, R Sathya Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai600127 India

ISBN: (纸本)9798350333244

Lithiumion batteries are crucial to the functioning of contemporary society, since they are used in a wide variety of applications from powering electric cars to storing renewable energy. Yet, there is still a significant problem in making these batteries safe and long-lasting. Using an unique CNN-LSTM-DNN architecture, we offer a new approach for predicting the RUL of lithium-ion batteries in this research. Our method combines the strengths of CNNs for image analysis, LSTMs for sequence data, and DNNs for feature learning. By combining these models, we are able to outperform existing state-of-the-art approaches to estimating battery RUL. An extensive lithium-ion battery dataset from the Center for Innovative Product Lifecycle Management is used to assess the efficacy of our approach;the findings show a considerable decrease in identification mistakes and an increase in RUL identification performance. This research provides the path for a more accurate and efficient early warning system to be created, which will be crucial in preventing dangerous and short-lived lithium-ion battery failures in the future. © 2023 IEEE.

关键词： Ions

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Predictive Maintenance of Aircraft Engine using Empirical Mode Decomposition based Long Short Term Memory 2

Predictive Maintenance of Aircraft Engine using Empirical Mo...

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2nd International Conference on Data science and Information System, ICDSIS 2024

作者： Nabi, Shaik Abdul Naick, B. Raveendra Almusawi, Muntather Shruthi, B.S. Rana Veer Samara Sihman Bharattej, R. Avn Institute Of Engineering & Technology Department Of Computer Science And Engineering Telangana Hyderabad India Tirupati India The Islamic University Department Of Computers Techniques Engineering College Of Technical Engineering Najaf Iraq Nitte Meenakshi Institute Of Technology Department Of Civil Engineering Bengaluru India Saint Leo University Department Of Computer Science FL United States

ISBN: (纸本)9798350381665

In this modern era of Industry 4.0, efficiency and safety of industrial machines are a primary concern in trade sectors and prediction of Remaining Useful Life (RUL) of an engine helps to plan effectively maintenance operation. However, the Predictive Maintenance is to acquire reliable prognostics and estimation RUL in turbofan engines is a difficult task. To solve this problem, an Empirical Mode Decomposition based Long Short-Term Memory (EMD-LSTM) for prediction of RUL in aircraft engine utilizing C-MAPSS dataset. Initially, the most relevant sensor signals are selected by Correlation analysis and filtered using Moving Median Filter (MMF). After, the signals are normalized by Z-score technique to train the prediction model and signals are fed as input to proposed EMD-LSTM approach for prediction of RUL span of the engine. The evaluation results of the proposed method using performance metrics are Root Mean Square Error (RMSE), Score, Accuracy and Precision. The presented EMD-LSTM model achieved great accuracy of 99.3%, Precision of 98.9%, RMSE of 13.02 and Score of 282.0 which is greater than the previous models like Deep Convolutional Neural Network (DCNN)-LightGBM, LSTM, Graph CN (GCN), DCNN and Denoising Online Sequential Extreme Learning Machine (DOSELM). © 2024 IEEE.

关键词： Empirical mode decomposition

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Layer-Wise Learning Rate Optimization for Task-Dependent Fine-Tuning of Pre-Trained Models: An Evolutionary Approach

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ACM Transactions on Evolutionary Learning and Optimization 2024年第4期4卷 1-23页

作者： Bu, Chenyang Liu, Yuxin Huang, Manzong Shao, Jianxuan Ji, Shengwei Luo, Wenjian Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data Ministry of Education and School of Computer Science and Information Engineering Hefei University of Technology Hefei China School of Artificial Intelligence and Big Data Hefei University Hefei China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies School of Computer Science and Technology Harbin Institute of Technology Shenzhen China

The superior performance of large-scale pre-Trained models, such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-Trained Transformer (GPT), has received increasing attention in both academic and industrial research and has become one of the current research hotspots. A pre-Trained model refers to a model trained on large-scale unlabeled data, whose purpose is to learn general language representation or features for fine-Tuning or transfer learning in subsequent tasks. After pre-Training is complete, a small amount of labeled data can be used to fine-Tune the model for a specific task or domain. This two-stage method of "pre-Training+fine-Tuning"has achieved advanced results in natural language processing (NLP) tasks. Despite widespread adoption, existing fixed fine-Tuning schemes that adapt well to one NLP task may perform inconsistently on other NLP tasks given that different tasks have different latent semantic structures. In this article, we explore the effectiveness of automatic fine-Tuning pattern search for layer-wise learning rates from an evolutionary optimization perspective. Our goal is to use evolutionary algorithms to search for better task-dependent fine-Tuning patterns for specific NLP tasks than typical fixed fine-Tuning patterns. Experimental results on two real-world language benchmarks and three advanced pre-Training language models show the effectiveness and generality of the proposed framework. © 2024 held by the owner/author(s).

关键词： Semantics

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Attentive Implicit Relation Embedding for Event Recommendation in Event-based Social Network 23

Attentive Implicit Relation Embedding for Event Recommendati...

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4th International Conference on Computing, Networks and Internet of Things, CNIOT 2023

作者： Liang, Yuan School of Computer Science and Engineering North China Institute Science and Technology Hebei China

ISBN: (纸本)9798400700705

The event-based social network (EBSN) is a new type of social network that combines online and offline networks, and its primary goal is to recommend appropriate events to users. Most studies do not model event recommendations on the EBSN platform as graph representation learning, nor do they consider the implicit relationship between events, resulting in recommendations that are not accepted by users. Thus, we study graph representation learning, which integrates implicit relationships between social networks and events. First, we propose an algorithm that integrates implicit relationships between social networks and events based on a multiple attention model. Then, the user modeling and event modeling models are fused using a multiattention joint learning mechanism to capture the different impacts of social and implicit relationships on user preferences, improving the recommendation quality of the recommendation system. Finally, the effectiveness of the proposed algorithm is verified in real datasets. © 2023 ACM.

关键词： User profile

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Detection of Plant Disease Using Machine Learning and Deep Learning Algorithms 8

Detection of Plant Disease Using Machine Learning and Deep L...

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8th IEEE International Conference on science, technology, engineering and Mathematics, ICONSTEM 2023

作者： Gollapudi, Kalyani Varigonda, Preethi Jancy, S. Pushpalatha, S. Marya, Viji Amutha Helen, Suji Sathyabama Institute of Science and Technology Computer Science and Engineering Chennai India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India Saveetha School of Engineering Simats Department of Data Analytics Chennai602105 India

ISBN: (纸本)9798350347791

Agriculture performs an critical position in India's economic system. Early detection of plant illnesses is critical to save you crop damage and similarly spread of diseases. Most plants, along with apple, tomato, cherry, grape, show symptoms of leaf ailment. These visible patterns may be found to correctly predict the disorder and take early movement to save you it. This can be triumph over with system getting to know and deep getting to know algorithms. We therefore recommend a method that determines tomato plant disease from pix of leaves. This method is performed with aid vector device (SVM), random woodland gadget studying algorithm, and look at algorithms Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and ResNet that is one of the switch learning techniques. Snoring. After the facts set is processed by way of the algorithms, the accuracy of the algorithms is compared and the snapshots are categorized. © 2023 IEEE.

关键词： Recurrent neural networks

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Statistical Detection of Malicious iOS Apps through Ensemble and Active Learning 2023

Statistical Detection of Malicious iOS Apps through Ensemble...

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15th International Conference on Contemporary Computing, IC3 2023

作者： Bhatt, Arpita Jadhav Agarwal, Parul Department of Computer Science & Engineering and Information Technology Jaypee Institute of Information Technology India

ISBN: (纸本)9798400700224

iOS is one of the most broadly used mobile operating systems after Android. In today's era, smartphones are widely used to perform several tasks such as net banking, GPS tracking, ordering products, etc. One of the main features of smartphones is that their capabilities can be expanded by installing third-party apps. However, these apps pose privacy threats to the user as they contain a lot of user's personal information, which may be vulnerable to attackers. Background studies have identified that a lot of user's personal and sensitive data is stored on smartphones and thus, they have become a perfect target for privacy threats in the whole mobile ecosystem. Machine learning techniques to detect malware apps have offered promising results for the Android platform whereas nominal work has been done for the iOS platform. In this paper, we are incorporating various machine learning techniques to detect malicious iOS apps from several categories. Some of the ensemble learning techniques with different classification algorithms are inculcated to enhance the precision of classifiers. A weighted average method on active and ensemble learning has been proposed which proved to be more efficient as compared to other machine learning techniques in most of the categories. Our empirical analysis has demonstrated that the proposed technique is statistically significant than active and ensemble learning. © 2023 ACM.

关键词： Smartphones

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