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

作者： Chhetri, Ahilya Surya, Sumukh Mohan Krishna, S. Rao, Vidya Cactus Communications Engineering and Technology Mumbai India Bosch Global Software Technologies Pvt. Limited Bengaluru India Aced Alliance University Department of Electrical and Electronics Engineering Bengaluru India Manipal Institute of Technology Manipal Academy of Higher Education Department of Data Science and Computer Applications Manipal India

ISBN: (纸本)9798350313123

Compared to conventional means of transport thatuse fossil fuels, electric vehicles are known to reduce pollution levels as they take power from renewable sources of energy stored in energy storage devices like batteries and fuel cells. Core temperature estimation of batteriesis extremely important inbattery management systems for preventing thermal runaway and ensuring safe operation. In this study, core temperature is estimated using a Kalman filter for two different battery chemistries viz;lithium polymer and lithium iron phosphate using a second-order thermal model. Further, a linear regression model is applied to verify the prediction over the trained and tested dataset. The lithium iron phosphate prediction curvehad a fit of approximately 82-83%, whilelithium polymerhad a fit of 72-82% during the charging and discharging of OCV-SOC variation. © 2023 IEEE.

关键词： Simulink

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Intelligent Voice Assistant by Using OpenCV Approach 2

Intelligent Voice Assistant by Using OpenCV Approach

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2nd International Conference on Electronics and Sustainable Communication Systems, ICESC 2021

作者： Saibaba, Ch.M.H. Waris, Saiyed Faiayaz Raju, S.Hrushikesava Sarma, Vsrk Jadala, Vijaya Chandra Prasad, Chitturi Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram522502 India Gandhi Institute of Technology and Management Department of Computer Science and Engineering Andhra Pradesh Gandhi Nagar530045 India

ISBN: (纸本)9781665428675

The basic plan of our work is to design and implement Artificial Intelligence based Voice Recognition System Software and to create a bundle that consists integration of several tasks, where we can process and execute using Clients Individual voice command. An Artificial Intelligent based Virtual or Personal Assistant were indicated as IVA or IPA could be a package of Intelligent mechanism which execute different tasks and services based on the queries and command for operating intelligent based system. It is identified as square measure at some areas of disciplines utilized for voice detection, where voice recognition is identified as communication channel between persons will be notified by machine. where machines can identify voice of a person wherever the voice is only approach of double communication, and a lot of typically, it permits to free-up each hands and vision doubtless for performing an additional action in comparison or facilitates conjointly inactivated individuals. In this paper we concentrated on the different artificial intelligent technologies that support the voice recognition and natural language processing. Generally, to try and do such quite tasks we would like a voice assistant to prefer and to get gadgets like Alexa. However identical practicality will be done by several the powerful packages like pywhatkit, Wikipedia, pyttsx3, pygame, speech recognition, OpenCV etc., we majorly focused on the practical implementation of voice recognition module based on the OpenCV. In modern technology speech recognition, object identification, digital image processing, language processing plays a major role in security and other factors, the most common mechanisms in artificial intelligence in association with machine and deep learning is that the mechanisms for identifying the natural language of a personalities. Latest terminology during present issue will cause artificial intelligence-based human-robot interaction that is based on multi-disciplinary te

关键词： Speech recognition

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Application of Neural Networks for Network Traffic Monitoring and Analysis 8

Application of Neural Networks for Network Traffic Monitorin...

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8th International Symposium on Multidisciplinary Studies and Innovative technologies, ISMSIT 2024

作者： Kushnerov, Oleksandr Murr, Pierre Herasymov, Serhii Milevskyi, Stanislav Melnyk, Marharyta Golovashych, Sergii Sumy State University Department of Economic Cybernetics Sumy Ukraine International University of Science and Technology in Kuwait Computer Engineering Department Kuwait Kuwait 'Kharkiv polytechnic institute' Cybersecurity department National Technical University Kharkiv Ukraine Set University Department of Cyber Security and information protection Kyiv Ukraine National Technical University 'Kharkiv polytechnic institute' Department of Software Engineering and Management Intelligent Technologies Kharkiv Ukraine

ISBN: (纸本)9798350354423

Monitoring and analyzing network traffic are critically important tasks in the context of increasing cyber threats. Traditional data processing methods often struggle to handle the large volumes and complexity of modern network traffic, necessitating the use of advanced technologies such as neural networks and machine learning. This article demonstrates the application of neural networks for monitoring and analyzing network traffic, highlighting their ability to learn from large datasets to detect complex patterns and anomalies. Using TensorFlow, we developed a neural network model based on an autoencoder that effectively identifies and classifies anomalies in network traffic. The model's effectiveness was validated using a synthetic dataset from Kaggle, showing high accuracy on both training and testing data. Our approach significantly improves the efficiency and accuracy of threat detection compared to traditional methods, making it a valuable tool for enhancing cybersecurity. © 2024 IEEE.

关键词： Cyber attacks

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Unveiling Market Anomalies: Harnessing Convolutional Neural Networks for Fraud Detection in Finance 3

Unveiling Market Anomalies: Harnessing Convolutional Neural ...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Thakre, N.K. Misba, M. Sajja, Sri Lavanya Fardale, Shyam K Vuyyuru, Veera Ankalu Mohamed Faizal, M.K. Yeshwantrao Chavan College of Engineering Department of Mathematics & Humanities Nagpur India Tamil Nadu Chennai India School of Computer Science and Engineering Secunderabad India Dmims Department of Business Management M.S. Nagpur India Koneru Lakshmaiah Education Foundation Department of Cse A.P. Vaddeswaram India K. Ramakrishnan College of Engineering Department of Ai & Ds Tamil Nadu India

ISBN: (纸本)9798331528201

The process of financial fraud detection is essential for the sound functioning of financial markets. Conventional techniques cannot detect more complex forms of fraud, as such advanced techniques are necessary. This research aims to explore a new technique based on CNNs with attention mechanisms for improved fraud-detecting techniques. Transaction data from Kaggle which contain a diverse range of purchases and market trends were gathered and scaled through Min-Max normalization. The attention mechanism was used for feature selection and control over noise at the same time, focusing on important features. The developed CNN model has trained to identify the higher order feature of data and it shows a prominent performance of detecting the fraudulent activities. The proposed model has high accuracy of 99.12% which surpass traditional techniques and basic models like TransUNet, MedT, and FAT-Net by 19.36%. Systematic performance appraisal with proven examples as to how it may be applied served as the basis for this study. Based on the results of the current paper, the efficiency of CNNs in detecting market irregularities, and the importance of deep learning methods in fraud detection in finance are highlighted. This research was developed in Python to strengthen the security of financial systems, and ultimately promoting more trust and accuracy to the markets of finance across the globe. © 2024 IEEE.

关键词： Convolutional neural networks

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Improved Multi-Face Detection with ResNet for Real-World Applications

Improved Multi-Face Detection with ResNet for Real-World App...

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International Conference on System Modeling & Advancement in Research Trends (SMART)

作者： Munish Kumar Monali Gulhane Sandeep Kumar Harshita Sharma Rashmi Verma Deepika Verma Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram India Department of Computer Science and Engineering Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune Nagpur India Department of Computer Science and Engineering DPG Institute of Technology and Management Gurugram India Department of Computer Science and Engineering Om Sterling Global University Hisar Haryana India

This paper presents an improved approach to multi-face detection using ResNet50 in Python for real-world applications. The study focuses on enhancing the accuracy and efficiency of detecting multiple faces in a single frame, a challenge often encountered in real-world scenarios such as surveillance and social media platforms. The proposed method leverages the power of ResNet, a deep residual learning framework known for its superior performance in image recognition tasks. The paper discusses the implementation details of the ResNet model in Python and how it has been optimized for multi-face detection. Experimental results demonstrate that the improved ResNet model outperforms traditional face detection methods regarding accuracy and speed, making it a promising solution for real-world applications. The paper concludes with potential future work and improvements that could further enhance the model's performance.

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Medical Diagnostic System using Embedding JADE in jCOLIBRI

Medical Diagnostic System using Embedding JADE in jCOLIBRI

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Electrical Electronics and Computing technologies (ICEECT), International Conference on

作者： Jagjit Singh Dhatterwal Kiran Malik Purushottam Das Kuldeep Singh Kaswan Department of Artificial Intelligence & Data Science Koneru Lakshmaiah Education Foundation Vaddeswaram AP India Department of Computer Science & Engineering (AIML) GL Bajaj Institute of Technology & Management Greater Noida India Department of Computer Science & Engineering Graphic Era Hill University Research Scholar Graphic Era Deemed to be University Dehradun India Department of Computer Science & Engineering Galgotias University Greater Noida India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

Case-based reasoning (CBR) methodology presents a foundation for a new technology of building intelligent computer-aided diagnoses systems. This Technology directly addresses the problems found in the traditional Artificial Intelligence (AI) techniques, e.g., the problems of knowledge acquisition, remembering, robustness and maintenance. This paper discusses the CBR methodology, the research issues and the technical aspects of implementing intelligent medical diagnoses systems. It develops a celebrative framework of MAS-CBR to criticize the medical problem to get the best treatment against dengue disease. This paper also embedding the framework of JADE and jCOLIBRI to improve the quality of decision- making in the medical diagnosis field.

关键词： Knowledge acquisition Knowledge based systems System integration Cognition Robustness Maintenance Medical diagnosis Informatics Medical diagnostic imaging Viruses (medical)

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Decision Making and Recognition of String Characters using NN-Fuzzy Implication System

Decision Making and Recognition of String Characters using N...

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3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024

作者： Henge, Santosh Kumar Bhagat, Avinash Mandal, Sanjeev Kumar Viraati, Nikhil Reddy Singh, Ravleen Kedhar, K. Rushi Department of Computer Science and Engineering School of Computer Science and Artificial Intelligence SR University Warangal506371 India School of Computer Applications Lovely Professional University Punjab Phagwara India Bangalore India Department of Data Insights Crisp Shared Services 7160 Columbia Gateway Dr Columbia United States Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation AP Vaddewaram India BE-CSE RV Institute of Technology and Management Bengaluru India

This research proposes the neural network (NN)-fuzzy logic control (FLC)-based methodology, which is designed with two stages of execution. Stage-1 is composed with the NN approach;it takes the input from the scanned image, the input from the classified clustered group, processes it with hidden layers, and generates the final output, which is input to the FLC in stage 2. The input of stage 2 converts the input into fuzzified data (0s and 1s) and trains the FLC system with fuzzified set values. The trained data has been simulated for further stages in the FLC-based decision-making approach and integrated to identify the character patterns based on the trained dataset. FLC-based planting and normalization techniques are integrated for re-training the data. The dataset has been framed with more than 6200 handwritten image characters, which were written by 235 writers. It aligned with the pre-processing, classification, and filtering stages and achieved a 99.8% recognition rate. © 2025 The Author(s).

关键词： Optical character recognition

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Reliability Assurance for Deep Neural Network Architectures Against Numerical Defects

arXiv

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arXiv 2023年

作者： Li, Linyi Zhang, Yuhao Ren, Luyao Xiong, Yingfei Xie, Tao Department of Computer Science University of Illinois Urbana-Champaign United States Department of Computer Sciences University of Wisconsin-Madison United States School of Computer Science Peking University China Key Laboratory of High Confidence Software Technologies Ministry of Education [Peking University China

With the widespread deployment of deep neural networks (DNNs), ensuring the reliability of DNN-based systems is of great importance. Serious reliability issues such as system failures can be caused by numerical defects, one of the most frequent defects in DNNs. To assure high reliability against numerical defects, in this paper, we propose the RANUM approach including novel techniques for three reliability assurance tasks: detection of potential numerical defects, confirmation of potential-defect feasibility, and suggestion of defect fixes. To the best of our knowledge, RANUM is the first approach that confirms potential-defect feasibility with failure-exhibiting tests and suggests fixes automatically. Extensive experiments on the benchmarks of 63 real-world DNN architectures show that RANUM outperforms state-of-the-art approaches across the three reliability assurance tasks. In addition, when the RANUM-generated fixes are compared with developers’ fixes on open-source projects, in 37 out of 40 cases, RANUM-generated fixes are equivalent to or even better than human fixes. © 2023, CC BY.

关键词： Defects

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Next-Generation Stock Market Prediction: Integrating CNN and LSTM Model 2

Next-Generation Stock Market Prediction: Integrating CNN and...

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2nd IEEE International Conference on Data science and Network Security, ICDSNS 2024

作者： Agnihotri, Kuldeep Yadav, Sameer Dahiya, Suman Subramanyam, M. Patil, Harshal Kalra, Gourav Isba Group of Institutes Madhya Pradesh Indore India University of Allahabad Department of Commerce and Business Administration Uttar Pradesh Prayagraj India Universal Ai University Department of Management Maharashtra India Koneru Lakshmaiah Education Foundation Department of Commerce AP Vaddeswaram India Iilm University Department of Computer Science and Engineering Uttar Pradesh Greater Noida India Department of Mechanical Engineering Ambala Mullana India

ISBN: (纸本)9798350373110

Stock market forecasting is one of the most challenging tasks in the financial industry due to the non-parametric nature of the stock market's time series, which is also complex, dynamic, chaotic, volatile, noisy, and otherwise non-linear. Fortunately, with the help of modern computing, intelligent models can assist expert analysts and investors in reducing investment risk. Preprocessing, feature selection, and training the model are the three stages that make up this suggested approach. When preprocessing data, the empirical wavelet transform decomposition algorithm is employed. When it comes to feature selection, one unsupervised and competitive learning algorithm that has proven effective is the self-organizing feature map. The model was trained using a CNN-LSTM. The proposed technique outperforms CNN and LSTM with an average accuracy of 90.56 percent. © 2024 IEEE.

关键词： Marketplaces

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A Knowledge-and-Data-Driven Modeling Approach for Mobile Payment Mode Recognition

A Knowledge-and-Data-Driven Modeling Approach for Mobile Pay...

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2024 China Automation Congress, CAC 2024

作者： Zhang, Li-Na Fan, Xing-Rong Zhang, Yu-Bu Engineering Research Centre for Waste Oil Recovery Technology and Equipment Ministry of Education Asset Management Department Chongqing Technology and Business University Chongqing China Engineering Research Centre for Waste Oil Recovery Technology and Equipment Ministry of Education Chongqing Technology and Business University Chongqing China School of Artificial Intelligence School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing China

ISBN: (纸本)9798350368604

Identifying different mobile payment modes based on cell phone signaling data in user movement is crucial for mobile payment scenario planning, payment risk assessment, and analyzing payment behavior patterns across multiple users. This paper proposes a novel knowledge-and-data-driven modeling approach to optimize the use of signaling data for mobile payment mode recognition. The approach integrates both unparsed signaling data and domain-specific URL-Key knowledge extracted from parsed signaling data that contains payment-related information. First, we develop a data-driven model for mobile payment event recognition, leveraging cell phone signaling data. Next, we establish a knowledge-driven model that utilizes domain URL-Key knowledge to identify mobile payment modes during user movement. These two models are then integrated using a composite coupling operator, and real data from a mobile telecommunication company located in Chongqing are used to calibrate and test our approach. Experimental results show that our approach significantly enhances both performance and efficiency. © 2024 IEEE.

关键词： Risk assessment

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