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10th International Conference on Electrical Energy Systems, ICEES 2024

作者： Mekala Rani, T.M.S. Suresh, A. Bhargavi, S. Vardhan Reddy, M. Harsha Sai Nikhil, K. Priya, G. Chandu Siddharth Institute of Engineering & Technology Department of Computer Science and Engineering Andhra Pradesh India Andhra Pradesh India

ISBN: (纸本)9798350353778

The project aims to enhance the security of cryptocurrency transactions through the implementation of advanced machine learning methodologies that are RNN(recurrent neural networks),LSTM,VGG16. We aim to create a strong and flexible system that can recognize and stop fraudulent activity in real-time by utilizing machine learning techniques. By utilizing dataset containing the features related to average time between sent and recived transactions, amount involved. This project focuses on creating a proactive defense mechanism that can analyze transaction patterns, detect anomalies, and promptly alert users or intervene to mitigate potential threats. By combining cutting-edge machine learning techniques with the ever-evolving landscape of cryptocurrency security, this project aims to safeguard digital assets and promote trust in the rapidly growing crypto ecosystem. © 2024 IEEE.

关键词： Adversarial machine learning

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An Epileptic Seizure Detection System based on Mediapipe and Deep Learning 2

An Epileptic Seizure Detection System based on Mediapipe and...

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2nd IEEE International Conference on Interdisciplinary Approaches in technology and Management for Social Innovation, IATMSI 2024

作者： Venkatesh, Abhijit Sajini, S. Srm Institute of Science and Technology Department of Computer Science and Engineering RamapuramBharati Salai Chennai600089 India

ISBN: (纸本)9798350360523

Epilepsy, a neurological disorder characterized by recurrent seizures, poses significant challenges in timely intervention and patient safety, affecting millions of individuals worldwide. Early and accurate detection of seizures is crucial for timely medical intervention and improved patient outcomes. This system holds particular significance for the elderly demographic, wherein prompt assistance is imperative to safeguard their lives. This study integrates MediaPipe Pose Detection alongside Machine Learning and Deep Learning techniques to enable the identification of individuals experiencing generalized epileptic seizures within the confines of their residences. Moreover, hyperparameter tuning has been performed on the machine learning algorithms to find optimal combination of hyperparameters for each algorithm. Experimental results show that highest average accuracy among machine learning models is achieved by SVM Classifier and Gradient Boosting algorithms at 88.08%, while Time series classification implemented using 1DCNN performs at 84.77% accuracy. It is noted that more varied data samples and standardized datasets would likely improve current results. © 2024 IEEE.

关键词： Patient treatment

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Ultimately Bounded Output Feedback Control for Networked Nonlinear Systems With Unreliable Communication Channel: A Buffer-Aided Strategy

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1566-1578页

作者： Yuhan Zhang Zidong Wang Lei Zou Yun Chen Guoping Lu the College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdao 266590China IEEE the Department of Computer Science Brunel UniversityUB83PHUnited Kingdom the College of Information Science and Technology Donghua UniversityShanghai 201620 the Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of EducationShanghai 201620China the School of Automation Hangzhou Dianzi UniversityHangzhou 310018China the School of Electrical Engineering Nantong UniversityNantong 226019China

This paper concerns ultimately bounded output-feedback control problems for networked systems with unknown nonlinear dynamics. Sensor-to-observer signal transmission is facilitated over networks that has communication *** transmissions are carried out over an unreliable communication channel. In order to enhance the utilization rate of measurement data, a buffer-aided strategy is novelly employed to store historical measurements when communication networks are inaccessible. Using the neural network technique, a novel observer-based controller is introduced to address effects of signal transmission behaviors and unknown nonlinear *** the application of stochastic analysis and Lyapunov stability, a joint framework is constructed for analyzing resultant system performance under the introduced controller. Subsequently, existence conditions for the desired output-feedback controller are delineated. The required parameters for the observerbased controller are then determined by resolving some specific matrix inequalities. Finally, a simulation example is showcased to confirm method efficacy.

关键词： Buffer-aided strategy neural networks nonlinear control output-feedback control unreliable communication channel

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A Self-Adapting and Efficient Dandelion Algorithm and Its Application to Feature Selection for Credit Card Fraud Detection

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 377-390页

作者： Honghao Zhu MengChu Zhou Yu Xie Aiiad Albeshri IEEE School of Computer and Information Engineering Bengbu UniversityBengbu 233030China School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China Helen and John C.Hartmann the Department of Electrical and Computer EngineeringNew Jersey Institute of TechnologyNewark NJ 07102 USA College of Information Engineering Shanghai Maritime UniversityShanghai 201306China Department of Computer Science King Abdulaziz UniversityJeddah 21481Saudi Arabia

A dandelion algorithm(DA) is a recently developed intelligent optimization algorithm for function optimization problems. Many of its parameters need to be set by experience in DA,which might not be appropriate for all optimization problems. A self-adapting and efficient dandelion algorithm is proposed in this work to lower the number of DA's parameters and simplify DA's structure. Only the normal sowing operator is retained;while the other operators are discarded. An adaptive seeding radius strategy is designed for the core dandelion. The results show that the proposed algorithm achieves better performance on the standard test functions with less time consumption than its competitive peers. In addition, the proposed algorithm is applied to feature selection for credit card fraud detection(CCFD), and the results indicate that it can obtain higher classification and detection performance than the-state-of-the-art methods.

关键词： Credit card fraud detection(CCFD) dandelion algorithm(DA) feature selection normal sowing operator

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A critical review on techno-economic analysis of hybrid renewable energy resources-based microgrids

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Journal of engineering and Applied science 2023年第1期70卷 148页

作者： Manas, Munish Sharma, Shivi Reddy, K. Shashidhar Srivastava, Abhinav Department of Electrical Engineering School of Engineering and Technology Central University of Haryana Mahendragarh India Department of Computer Science and Engineering Jain University Bangalore India Department of Electrical and Electronics Engineering CVR College of Engineering Hyderabad India

Now that the population is growing, the expenditure on basic needs of life is also increasing due to a lack of or less availability of resources. The economy consumed electricity is reaching peaks as its main fuel, coal, is decreasing day by day. Due to this, 90% of the population who are in the middle class, lower middle class, or rural areas are economically poor and are unable to bear the prices. To overcome the financial problems, many researchers have prepared various types of microgrids that generate electricity from various types of flow resources, like hydro, solar, biogas, and air current power stations, whose system is called a compound flow power system. This paper gives a combined review of various research papers that discuss some case studies and some research on various models designed on software like HOMER Pro, how microgrids become economic barriers, optimal power supply solutions with CFPS, distributed and centralized microgrid components, the technical and economic feasibility of EV charging stations, and the analysis of various combinations of power systems at various locations like Bangladesh, Canada, the Republic of Djibouti, China, Indonesia, Sierra Leone, some rural sites in India, and some developing countries. This overview provides a glimpse into the various aspects of CFPS, including fusion approaches, techno-economic analysis, simulation platforms, storage technologies, design specifications, unit sizing methodologies, and control techniques. Further research and analysis in these areas are needed to explore their applications and advancements in CFPS development. The main reason for the study is to analyze and bring various ideas and models of various researchers together on a common platform and make a combined conceptual framework for further proceedings. © 2023, The Author(s).

关键词： Renewable energy resources

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Vitamin Deficiency Detection Using Neural Networks 9

Vitamin Deficiency Detection Using Neural Networks

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9th International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2024

作者： Krishna Chaithanya, T. Gupta, Jiniya Roobini, M.S. Department of Computer Science and Engineering Chennai600119 India Sathyabama Institute of Science and Technology Chennai600119 India

ISBN: (纸本)9798350350845

Vitamin deficiency is a pervasive health issue affecting millions globally, often leading to severe health complications if undiagnosed. Various vitamin deficiencies can be identified by identifiable symptoms manifesting in numerous areas across the human body. Blood tests are most commonly used to detect deficiencies. However, blood tests are expensive and have limited availability. The project aims to develop an automated system to detect vitamin deficiencies using Mobilenet, convolutional neural networks (CNN), NasNet-mobile, inception modules, and artificial neural networks (ANN). The program allows users to detect potential vitamin deficiencies without needing blood samples by examining images of the eyes, lips, tongue, and nails. Here we have considered the dataset of abnormalities caused by vitamin deficiencies like glaucoma eyes, bluish nails, and other abnormalities of lips and tongue. We have trained the model on these image datasets, by leveraging image recognition and data analytics, the proposed system offers a quick, efficient, and non-invasive method of detection thereby streamlining the diagnostic process and facilitating early intervention. The aim is to provide an economical, accessible, and efficient clinical screening method. Moreover, the platform furnishes tailored dietary suggestions to tackle pinpointed inadequacies, thus alleviating health hazards linked with insufficient nourishment. By furnishing an easily reachable resource for prompt identification and action, our system aids in tackling a prevalent worldwide health concern. The program functions as a beneficial aid for individuals to combat a widespread global challenge, impacting millions due to inadequate comprehension of nutrition. Serving as a valuable resource, it addresses an international issue, assisting those facing nutritional uncertainties worldwide. © 2024 IEEE.

关键词： Blood

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Play Store App Rating Prediction Using Machine Learning & Deep Learning Regression Techniques 2

Play Store App Rating Prediction Using Machine Learning & De...

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2nd IEEE International Conference on Futuristic Technologies, INCOFT 2023

作者： Biswal, Bhramara Bar Das, Satya Narayan Sahu, Satyanarayan Banerjee, Shobhan Computer Science and Engineering Gandhi Institute of Engineering and Technology Gunupur India Department of Engineering Technology Birla Institute of Technology and Science Pilani India

ISBN: (纸本)9798350308846

The rating & review of an app in the App Store or Play Store plays an essential role for end users to get information about the app based on other people's experiences with that app. The reviews might be context-based, but the ratings give a firsthand impression to the users, simply looking at scores out of five. In this paper, we have performed multivariate analysis on the Google Play Analysis Dataset to determine the critical features and prune off the redundant ones, followed by which we compared the accuracy of various regression models in predicting the ratings of various apps in the dataset. © 2023 IEEE.

关键词： Artificial Neural Network Feature Importance Multivariate Analysis Rating prediction Regression

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Regional Heatwave Prediction Using Deep Learning Models In Bangladesh 2

Regional Heatwave Prediction Using Deep Learning Models In B...

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2nd International Conference on Information and Communication technology, ICICT 2024

作者： Sultana, Syeda Samia Mumu, Sumaiya Siddiqua Fahim, Rafid Reezwan Neha, Noshin Nawar Mahbub, Md. Shahriar Department of Computer Science and Engineering Ahsanullah University of Science and Technology Tejgaon Industrial Area Dhaka1208 Bangladesh

ISBN: (纸本)9798331508227

With the growing threat of heatwaves in Bangladesh due to climate change, predicting heatwave days has become vital for effective mitigation measures. In this study, a robust dataset of 25 years, acquired through the Bangladesh Meteorological department, is capitalized to explore Long-Short Term Memory (LSTM), Bi-directional LSTM (Bi-LSTM), and Convolutional Neural Network (CNN) models for predicting heatwave in four heatwave-prone districts of Bangladesh: Dhaka, Rajshahi, Bogra and Dinajpur. The selected features are maximum temperature, humidity, dew point temperature, pressure, wind speed and sunshine. The dataset was divided into 80% for training, 10% for validation and 10% for testing. The predictive performance of BiLSTM, Conv1D, and LSTM models was evaluated with Accuracy, F1-score, R2 score. Bi-LSTM consistently showed high accuracy, with Dhaka and Rajshahi achieving 96.96% and 97.40%, respectively, and Bogra recorded the highest accuracy at 99.57%. In Dhaka, Bi-LSTM had the highest accuracy at 96.96%, followed by Conv1D at 95.65%, and LSTM at 93.04%. For the F1-score, Bi-LSTM led with 86.27%, and in R2 score, it performed best with 77.40%. These results demonstrate the superior performance of the Bi-LSTM model in predicting heatwave days, making it a beneficial tool for early warning and preparedness efforts. Despite the predominant use of statistical models on Bangladeshi datasets, this study explores the novel application of deep learning models like Bi-LSTM and CNNs for heatwave classification, which offer better identification of complex spatiotemporal patterns and are more resistant to anomalies. ©2024 IEEE.

关键词： Long short-term memory

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Deep Learning Based Traffic Sign Recognition using CNN and TensorFlow 5

Deep Learning Based Traffic Sign Recognition using CNN and T...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Gowtham, P.Srinivasa Bhargavi, Maridu Kavyanjali, P. Babu, P.Naga Subhash, K. Vignan's Foundation for Science Technology and Research Department of Computer Science And Engineering Vadlamudi AP Guntur India

ISBN: (纸本)9798331530013

It is very hard to detect small objects. When we are talking about self-driving car, then it is a 'driver less' or autonomous vehicle that functions independently without any human intervention. It makes use of hardware or software that can be used for the movement of vehicles from one destination to another. Even though the revolution of self-driving car is unstoppable, it is facing many challenges. The first challenge that we are facing is Traffic rule implementation. For detection and road traffic signals categorization in street view images: We are creating a deep neural network model that is used for describing road signs seen in images or videos. © 2024 IEEE.

关键词： Traffic signs

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Extended Deep Learning Algorithm for Improved Brain Tumor Diagnosis System

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Intelligent Automation & Soft Computing 2024年第1期39卷 33-55页

作者： M.Adimoolam K.Maithili N.M.Balamurugan R.Rajkumar S.Leelavathy Raju Kannadasan Mohd Anul Haq Ilyas Khan ElSayed M.Tag El Din Arfat Ahmad Khan Department of Computer Engineering Government Polytechnic CollegeSevvapetTiruvallurTamil Nadu602025India Department of Computer Science and Engineering KG Reddy College of Engineering and TechnologyHyderabadTelangana500075India Department of Computer Science and Engineering Sri Venkateswara College of EngineeringSriperumbudurChennai602117India Department of Computer Science and Engineering Annamalai UniversityChidambaramTamil Nadu608002India Department of Computer Science and Engineering Aarupadai Veedu Institute of TechnologyVinayaka Mission Research FoundationPaiyanurTamil Nadu603104India Department of Electrical and Electronics Engineering Sri Venkateswara College of EngineeringSriperumbudur602117India Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Mathematics College of Science Al-ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia Electrical Engineering Department Faculty of Engineering&TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of ComputingKhon Kaen UniversityKhon Kaen40002Thailand

At present,the prediction of brain tumors is performed using Machine Learning(ML)and Deep Learning(DL)*** various ML and DL algorithms are adapted to predict brain tumors to some range,some concerns still need enhancement,particularly accuracy,sensitivity,false positive and false negative,to improve the brain tumor prediction system ***,this work proposed an Extended Deep Learning Algorithm(EDLA)to measure performance parameters such as accuracy,sensitivity,and false positive and false negative *** addition,these iterated measures were analyzed by comparing the EDLA method with the Convolutional Neural Network(CNN)way further using the SPSS tool,and respective graphical illustrations were *** results were that the mean performance measures for the proposed EDLA algorithm were calculated,and those measured were accuracy(97.665%),sensitivity(97.939%),false positive(3.012%),and false negative(3.182%)for ten *** in the case of the CNN,the algorithm means accuracy gained was 94.287%,mean sensitivity 95.612%,mean false positive 5.328%,and mean false negative 4.756%.These results show that the proposed EDLA method has outperformed existing algorithms,including CNN,and ensures symmetrically improved *** EDLA algorithm introduces novelty concerning its performance and particular activation *** proposed method will be utilized effectively in brain tumor detection in a precise and accurate *** algorithm would apply to brain tumor diagnosis and be involved in various medical diagnoses *** the quantity of dataset records is enormous,then themethod’s computation power has to be updated.

关键词： Brain tumor extended deep learning algorithm convolution neural network tumor detection deep learning

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