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16th International Symposium on Computational Intelligence and Design, ISCID 2023

作者： Smieja, Piotr J. Lu, Jiang Sun, Qingquan Fu, Xingang Zhang, Ting Dept. of Electrical and Biomedical Engineering University of Nevada Reno Reno United States Department of Computer Engineering University of Houston Clear Lake Houston United States School of Computer Science and Engineering California State University San Bernardino San Bernardino United States Department of Computer Science University of Houston Downtown Houston United States

ISBN: (纸本)9798350317459

The diagnosis of early stages of diseases such as lung cancer or diabetes is challenging as those diseases don’t have many noticeable symptoms. The human breath, however, contains many VOCs (volatile organic compounds) that could be used as a clue for conducting the proper test. In this study, a custom breathalyzer device is developed for noninvasive detection and diagnosis of diabetes with the use of a human breath print. An array of 6 MOS sensors is used in the prototype to collect 8 VOCs of the breath print of the subjects. The k-nearest Neighbor algorithm is implemented to determine the likelihood of a disease. In the results, an accuracy of 93.6% was achieved. ©2023 IEEE.

关键词： Volatile organic compounds

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Beamforming and Phased Arrays: Elevating MIMO-OFDM systems for Next-Generation Wireless Networks

Beamforming and Phased Arrays: Elevating MIMO-OFDM Systems f...

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

作者： Deepak Upadhyay Kunj Bihari Sharma Mridul Gupta Abhay Upadhyay Satya Prakash Yadav Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Dept. of Electronics and Communication Engineering Graphic Era Deemed to be University Dehradun India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

This study explores the optimisation of Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems through the use of phased arrays and advanced beamforming methods. With a focus on overcoming the challenges that come with 5G wireless communication, like high data rate needs, interference resistance, and efficient spectrum usage, this work presents a novel approach that combines dynamic beam steering and adaptive precoding to significantly enhance system performance. Through thorough modelling, we demonstrate considerable improvements in bit error rates (BER), throughput, and spectrum efficiency compared to traditional MIMO-OFDM configurations. The results demonstrate a 25% improvement in throughput and a 40% reduction in BER in densely populated metropolitan environments, underscoring the potential of phased arrays and beamforming to further improve 5G network performance. This work not only validates the efficacy of the proposed enhancements but also establishes a foundation for future advancements in wireless communication systems by highlighting the critical roles that phased arrays and beamforming will play in achieving the maximum potential of 5G technology.

关键词： Phased arrays Beam steering Adaptive systems Array signal processing 5G mobile communication Precoding Wireless networks Bit error rate Interference Throughput

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TSPvis: A Temperature Sensitive Paint Formulation Visual Design system

TSPvis: A Temperature Sensitive Paint Formulation Visual Des...

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Pattern Recognition and Artificial Intelligence (PRAI), International Conference on

作者： Jing Liao Yadong Wu Guijuan Wang Fupan Wang Huarong Chen Li Long Dept. Computer Science and Technology Southwest University of Science and Technology Mianyang China Dept. Computer Science and Engineering Sichuan University of Science and Engineering Yibin China

The composition and proportion parameters of temperature sensitive paint formulation are complex. The current methods have a long development cycle and low efficiency. We design a visual formulation design system for temperature sensitive paint, helps desingers use historical data to improve the efficiency of formulation design. TSPvis provides a visual formulation design function, implements the proportion decision of formulation based on decision tree, and recommends reference for ingredients proportion to the formulation designer. We use the DPC clustering algorithm to quickly obtain the radial basis center, and use the Adam algorithm to improve the weight calculation from the hidden layer to the output layer, to improve RBF neural network. TSPvis use this RBF as a model to predict performance of the formulation which designed before, and feeds back to the designer to improve the formulation designing of temperature sensitive paint.

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Sentiment Analysis on Twitter Data of Omicron (B.l.l.529) using Natural Language Processing

Sentiment Analysis on Twitter Data of Omicron (B.l.l.529) us...

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2022 IEEE Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2022

作者： Datta, Suseta Mitra, Poojarini Kundu, Pritam Ministry of Electronics and Information Technology National Institute of Electronics and Information Technology Kolkata India University of Engineering & Management Dept. of Computer Applications Kolkata India University of Engineering & Management Dept. of Computer Science and Engineering Kolkata India

ISBN: (纸本)9781665477192

The COVID pandemic is causing outrageous interference in everyday life and financial activity. Close to two years after the presence of COVID, WHO allotted the variety B.l.l.529 a variety of concern, named 'Omicron'. Online diversion data assessment is created and transformed into a more renowned subject of investigation. In this paper, a sizably voluminous heap of appraisals and assessments are culminated with online redirection information. The evaluations and appearances of Twitter electronic diversion stage clients are summarised and researched by considering sentiment analysis by utilising various natural language processing techniques based on positive, negative, and neutral tweets. All potential outcomes are considered for investigating the feelings of Twitter clients. For the most part, tweets are assessed clearly, and this assessment ensures the headway of this investigation study. Different kinds of analyzers are utilised and measured. The 'TextBlob Sentiment Analyzer' has given the highest polarity score based on positivity, negativity, and neutrality rates in terms of inspiration, pessimism, and impartiality. A total dataset is fully determined and classified with all the analyzers, and a comparative result is also measured to find the ideal analyzer. It is intended to apply boosting machine learning methods to increase the accuracy of the proposed architecture before further implementation. © 2022 IEEE.

关键词： Sentiment analysis

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Uncertainty Aware Deep Learning Model for Secure and Trustworthy Channel Estimation in 5G Networks 12

Uncertainty Aware Deep Learning Model for Secure and Trustwo...

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12th Mediterranean Conference on Embedded Computing, MECO 2023

作者： Catak, Ferhat Ozgur Cali, Umit Kuzlu, Murat Sarp, Salih University of Stavanger Dept. of Electrical Engineering and Computer Science Stavanger Norway Norwegian University of Science and Technology Department of Electric Power Engineering Trondheim Norway Old Dominion University Electrical Engineering Technology NorfolkVA United States Virginia Commonwealth University Electrical and Computer Engineering RichmondVA United States

ISBN: (纸本)9798350322910

With the rise of intelligent applications, such as self-driving cars and augmented reality, the security and reliability of wireless communication systems have become increasingly crucial. One of the most critical components of ensuring a high-quality experience is channel estimation, which is fundamental for efficient transmission and interference management in wire-less networks. However, using deep neural networks (DNNs) in channel estimation raises security and trust concerns due to their complexity and the need for more transparency in decision-making. This paper proposes a Monte Carlo Dropout (MCDO)-based approach for secure and trustworthy channel estimation in 5G networks. Our approach combines the advantages of traditional and deep learning techniques by incorporating conventional pilot-based channel estimation as a prior in the deep learning model. Additionally, we use MCDO to obtain uncertainty-aware predictions, enhancing the model's security and trustworthiness. Our experiments demonstrate that our proposed approach outper-forms traditional and deep learning-based approaches regarding security, trustworthiness, and performance in SG scenarios. © 2023 IEEE.

关键词： 5G mobile communication systems

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Dynamic Road Management in the Era of CAV

arXiv

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arXiv 2024年

作者： Younis, Mohamed Lee, Sookyoung Lalouani, Wassila Tan, Dayuan Gupte, Sanket Dept. of Computer Science and Electrical Engineering University of Maryland Baltimore County BaltimoreMD United States Dept. of Computer Science and Engineering Ewha Womans University Seoul Korea Republic of

Traffic management and on-road safety have been a concern for the transportation authorities and the engineering communities for many years. Most of the implemented technologies for intelligent highways focus on safety measures and increased driver awareness, and expect a centralized management for the vehicular traffic flow. Leveraging recent advances in wireless communication, researchers have proposed solutions based on vehicle-to-vehicle (V2V) and vehicle-to-Infrastructure (V2I) communication in order to detect traffic jams and better disseminate data from on-road and on-vehicle sensors. Moreover, the development of connected autonomous vehicles (CAV) have motivated a paradigm shift in how traffic will be managed. Overall, these major technological advances have motivated the notion of dynamic traffic management (DTM), where smart road reconfiguration capabilities, e.g., dynamic lane reversal, adaptive traffic light timing, etc. will be exploited in real-time to improve traffic flow and adapt to unexpected incidents. This chapter discusses what the challenges in realizing DTM are and covers how CAV has revolutionized traffic management. Moreover, we highlight the issues for handling human-driven vehicles while roads are transitioning to CAV only traffic. Particularly, we articulate a new vision for inter-vehicle communication and assessment of road conditions, and promote a novel system for traffic management. Vehicle to on-road sensors as well as inter-vehicle connectivity will be enabled through the use of hand-held devices such as smartphones. This not only enables real-time data sharing but also expedites the adoption of DTM without awaiting the dominant presence of autonomous vehicle on the road. The proposed traffic management system incorporates computation and communication capabilities in traffic lights and road-side units, and accounts for the human-factor in controlling the traffic flow. The goal is to allow autonomous and driver-centric routing decis

关键词： Traffic congestion

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Stealth Encoding - Concealing data with the Least Significant Bit Method

Stealth Encoding - Concealing data with the Least Significan...

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Advancement in Computation & computer Technologies (InCACCT), International Conference on

作者： Bhaskar Kantapalli Siva Gnani Meruga Prasanthi Polagani Venu Babu Pingali Surya Teja Seelam dept. of computer science and engineering Seshadri Rao Gudlavalleru Engineering College Gudlavalleru INDIA dept. of computer science and engineering Seshadri Rao Gudlavalleru Engineering College Mandavalli INDIA dept. of computer science and engineering Seshadri Rao Gudlavalleru Engineering College Vijayawada INDIA dept. of computer science and engineering Seshadri Rao Gudlavalleru Engineering College Machilipatnam INDIA

Data security is a major issue for computer users these days. So, we conceal the textual data using steganography, the art and science of concealing information. In this case, we conceal both the message’s content and the fact that it is being sent. In order to make it challenging to Figure out that a hidden message is present in a specific film, this technique involves concealing secret data within a conventional, non-secret file. At its destination, the secret data is subsequently removed. Several uses of steganographic technology have distinct requirements. Here, we suggest a method for concealing data, including reverse data hiding for data extraction. The textual data will be secured in different frames of the video and audio using the Least Significant Bit (LSB) and related algorithms. We can conclude that the original data will never change the characteristics of video or audio even after applying steganography techniques.

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Credit Card Fraud Detection Utilizing Machine Learning, Deep Learning, and Bayesian Networks

Credit Card Fraud Detection Utilizing Machine Learning, Deep...

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Artificial Intelligence and Data engineering (AIDE), International Conference on

作者： Arra Keerthan Reddy Harsha Vardhan Rapeti Doduguru Amarnath Reddy M. Srinivas Computer Science Engineering Amrita Vishwa Vidyapeetham Amaravati India Dept. of Computer Science Amrita Vishwa Vidyapeetham Amaravati India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

Detecting credit card fraud is a critical challenge in the modern financial landscape, where robust and efficient solutions are essential to mitigate losses and safeguard consumers. This research evaluates and contrasts three primary approaches to this issue: Machine Learning (ML), Deep Learning (DL), and Bayesian Networks (BN). Using publicly available datasets, several ML techniques, including Logistic Regression, Support Vector Machines (SVM), Random Forests, Decision Trees, and XGBoost, were assessed alongside DL models such as Multi-Layer Perceptron (MLP) and Deep Neural Networks (DNNs). Additionally, a Bayesian Network was employed to analyze causal relationships between fraud-related features, offering an interpretable probabilistic framework. While ML and DL approaches achieved high performance regarding accuracy, precision, and recall, the Bayesian Network distinguished itself by providing enhanced interpretability and reliability. This study highlights the complementary strengths of these methods and the potential for their integration to develop adaptive and efficient fraud detection systems

关键词： Deep learning Support vector machines Logistic regression Accuracy Credit cards Probabilistic logic Bayes methods Fraud Ensemble learning Random forests

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Bayesian-Driven Deep Learning and Machine Learning Approaches for Handwritten Word Recognition

Bayesian-Driven Deep Learning and Machine Learning Approache...

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Artificial Intelligence and Data engineering (AIDE), International Conference on

作者： Medisetty Dinesh Lingamaneni Sagar Pallapu Moksha Pappula Baladhitya M. Srinivas Computer Science Engineering Amrita Vishwa Vidyapeetham Amaravati India Dept. of Computer Science Amrita Vishwa Vidyapeetham Amaravati India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

Identifying handwritten words poses a complicated problem owing to the variances in handwriting styles and the possible noise and distortions present in the data. The performance of used deep learning and traditional machine learning handwriting recognition methods for recognizing handwritten words is studied in this paper and the advantages of using deep learning are discussed. In particular, neural network models such as CNN and 2DLSTM networks had a great work in this area. In contrast, we detect whole handwritten words without the need of segments into standalone characters. The CNNs are used for feature extraction, the Bidirectional LSTMs for sequence prediction and output is decoded via a CTC layer. The model was evaluated over the IAM words dataset from the IAM handwriting database, achieving 87% accuracy and a character error rate of 7.77%. In addition, we experimented with ML techniques, including SVM, RF has got an accuracy of 72% and 80% accuracies, and a Bayesian network of 61% accuracy, to provide a comparative analysis.

关键词： Deep learning Support vector machines Radio frequency Handwriting recognition Accuracy Error analysis Neural networks Feature extraction Bayes methods Convolutional neural networks

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Car Defect Detection Using Image Processing

Car Defect Detection Using Image Processing

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Durai, S. Sujithra, T. Charan, Athmakuri Sai Pulyala, Sai Kiran Satyam, Battula Vishnuwardhan Vel Tech Rangarajan Dr. Sagunthala RandD Institute of Science and Technology Avadi Dept. of Cse Chennai India Manipal Institute of Technology Computer Science and Engineering Manipal India

ISBN: (纸本)9798350334357

Defect Detection became one of the hardest problems in this world nowadays. As of now humans detected the defects with the help of their naked eye. This process is time consuming and this can be done by only experts and these decisions may be differ from expert to expert. To avoid this problem we are training the collected data set. This will consume very less time and provides the best solution. The detection of defects is essential in quality control in manufacturing to get high quality products in less time. Humans can identify large defects but in case of micro defects humans can't found those so, this implementation will helpful in finding large as well as micro and milli defects as well. In this project we are trying to detect the defects such as dents and ripples present on the surface of the car which are macro in size at the time of manufacturing the cars and in the case of accidents happened. We'll use a high-quality camera to simultaneously capture photographs of the vehicle's five sides. We will use Convolution Neural Network [CNN] for training (which highlights the location of the defects). Once trained, that model we can use it for different end user applications. © 2023 IEEE.

关键词： Feature extraction

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