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2023 IEEE International Conference on Modelling, Simulation and Intelligent Computing, MoSICom 2023

作者： Ahmed, Sanober Sarfaraz Shetty, Sujala Birla Institute of Technology and Science Computer Science Engineering Dubai United Arab Emirates

ISBN: (纸本)9798350393415

The prediction of energy consumption in residential buildings has emerged as a crucial area of study in recent times, owing to the substantial demand for energy-efficient solutions. In this paper, a comparative analysis of diverse machine learning models is presented, encompassing Multilayer Perceptron (MLP), Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), Random Forest, Linear Regression, Autoencoder, Hybrid (Random Forest-MLP), Support Vector Machine (SVM), Ensemble, and K-Nearest Neighbors (KNN), for predicting energy consumption in residential buildings. The performance of each model is evaluated using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) values. The outcomes demonstrate that Artificial Neural Networks (ANN) outperforms all other models in terms of both RMSE and MAE values, making it a promising solution for predicting energy consumption in residential buildings. © 2023 IEEE.

关键词： Energy utilization

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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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Comparative Analysis of Decision Tree with Existing Approaches for Improving Prediction Performance 15

Comparative Analysis of Decision Tree with Existing Approach...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Pathan, Shabana Sharma, Sanjeev Kumar Department of Computer Science & Engineering Oriental University M.P Indore India Department of Computer Science and Engineering Oriental Institute of Science and Technology M.P Bhopal India

ISBN: (纸本)9798331300579

Data mining has applications for a wide range of enterprises, including those in the banking, telecommunications, energy, medical, marketing, and finance industries. The real-world situations involve noisy, unpredictable, ambiguous, and untrustworthy data, which further muddies the waters. During the information-gathering process, uncertainty arises in various application domains and is comprised of measurement errors, outdated information (data staleness), and repeated entries of the same type. This could lead to the prediction model performing poorly. The suggested method uses Deep Learning to control the uncertainty in the dataset before building Decision Tree models for categorization. This study compares two proposed approaches for decision tree optimization with the state-of-the-art approaches based on F1 score, accuracy, precision, recall, and evaluation parameters. © Grenze Scientific Society, 2024.

关键词： Prediction models

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Spotting Irregularities in Infinitesimal Images of Blood via Comparable Fragmentation of Image 4th

Spotting Irregularities in Infinitesimal Images of Blood via...

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4th international conference on Energy Systems, Drives and Automations, ESDA 2021

作者： Prasanna, Nissankara Lakshmi Velpula, Koteswararao Sanam, Narendra Department of Computer Science and Engineering VasireddyVenkatadri Institute of Technology Guntur India

ISBN: (纸本)9789819936908

Most chronic diseases resulting from the observation of individuals become some specific with their exact clinical picture. These portrayals are of assorted sorts, not out of the ordinary as minuscule pictures, ultrasound, registered tomography, attractive reverberation picture and so on [1, 2]. Barely, a few varieties from the standard can be arranged with horrendous cells in plasma or polyps or breakages or parasitic recognizing, etc. Finding anomalous partial expressions requires doctor’s skill;this corresponding distinctive features treated through medication claimed by a doctor or expert. In clinical minuscule portrayals, typical pieces other than sporadic parts are pooled. Random and conventional pieces are not really perceived by sporadic and conventional pieces of portrayals at occurrence;anomalies are debased among ordinary areas of portrayal. Such anomalies stay absentminded in firm parts for a clear area in the portrayals. These anomalies are not really safeguarded nor can cannot escape a thoughtful part genuinely in the portrayal. Irregularities are isolated close by customary pieces of pictures. Recognizing such irregularities is vital. In earlier methods [3–5], these irregularities are recognized and utilized BFS and Shortest Path Algorithm. This paper centers around recognizing irregularities utilizing comparable to discontinuity of clinical minute pictures. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image fragmentation Irregularities Sickle cell

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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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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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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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A Comparative Study of Object Detection Techniques for Automatic License Plate Recognition for Indian License Plates 5

A Comparative Study of Object Detection Techniques for Autom...

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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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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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A CNN Deep Learning Technique for Botnet Attack Detection for IoT Application 3

A CNN Deep Learning Technique for Botnet Attack Detection fo...

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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Batham, Yashi Kumar Tiwari, Rakesh Technocrats Institute of Technology and Science Dept. of Computer Science and Engineering Bhopal India

ISBN: (纸本)9798350322842

The Internet of Things (IoT) is a significant technology that enables us to develop many helpful online applications. The Internet of Things allows remote operating items using preexisting network infrastructure. Services that are based on information technology, computers, and other technologies are included in the cyber world. The cyber-world sees many protocols and technological advancements that lead to improvements. When it comes to cyber-based services, security is an extremely significant issue. Classification methods based on machine learning (ML) and deep learning (DL) can accurately forecast the most effective model for a dataset. A cyber assault on a wide scale carried out by devices infected with malware and managed remotely is known as a botnet attack. Under the context of IoT applications, an effective method for detecting botnet attacks is presented in this research. © 2023 IEEE.

关键词： Internet of things

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