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3D mmWave MIMO Channel Modeling and Reconstruction for Street Canyon and Highrise Scenarios

3D mmWave MIMO Channel Modeling and Reconstruction for Stree...

2024 International Conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Idowu-Bismark, Olabode Oshin, Oluwadamilola Adetiba, Emmanuel Idowu-Bismark, Oluwatobiloba Covenant University Dept of Electrical and Information Engineering Ota Nigeria University of Hertfordshire Department of Computer Science Welwyn HatfieldAL109JD United Kingdom

ISBN: (纸本)9798350358155

The use of millimeter-wave (mmWave) and full-dimensional multiple-input multiple-output (FD-MIMO) antenna systems for 3D wireless communication is being exploited for enhanced network capacity improvement in the ongoing fifth-generation (5G) deployment. For adequate assessment of competing air interface, random access channelization, and beam alignment procedures in mmWave systems, channel models for different use scenarios are necessary. A ray-tracing study was conducted with the use of a Wireless Insite ray tracing algorithm to characterize the mmWave channel in urban areas, using Lagos Island, Nigeria databases to predict measured statistics. These statistics include path loss, rms delay spread, angular spread of arrival, and departure in the azimuth and elevation domain. A 3GPP-style 3D mmWave channel is modeled and reconstructed, emphasizing the use of a ray tracer to determine elevation model parameters. Line of sight (LOS) and non-line of sight (NLOS) 3D models were developed for street canyon and high-rise scenarios © 2024 IEEE.

关键词： 5G mobile communication systems

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ChemGenX: AI in the Chemistry Classroom 24

ChemGenX: AI in the Chemistry Classroom

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2024 International Symposium on Artificial Intelligence for Education, ISAIE 2024

作者： Abbas, Touqeer Javed, Umair Mehmood, Faisal Raza, Muneeb Li, Hui Department of Computer Science and Technology Beijing University of Chemical Technology Beijing China School of Electrical and Information Engineering Zhengzhou University Henan Zhengzhou China Beijing China

ISBN: (纸本)9798400707100

Generative AI is reshaping education by offering personalized learning experiences and innovative teaching methods. In this study, we introduced ChemGenX, an AI tool designed to enhance chemistry education by generating molecular structures with user-defined features. The primary objective is to simplify molecular design and improve student learning. Our approach utilizes the coherently aligned multimodal semantic space using pre-trained CLIP;generate text features from image features to seamlessly alleviate the need for text conditioning, resulting in accurate predictions of bond lengths, angles, and molecular structures. The model was trained on a comprehensive molecular dataset, achieving competitive performance in zero-shot text-to-image conversion. Pilot studies with high school students and teachers demonstrate its potential in generating accurate molecular structures, including key features like bond lengths and angles. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Bond length

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Leveraging ChatGPT for enhanced stock selection and portfolio optimization

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Neural Computing and Applications 2025年第8期37卷 6163-6179页

作者： Huang, Zhendai Liao, Bolin Hua, Cheng Cao, Xinwei Li, Shuai College of Computer Science and Engineering Jishou University Jishou416000 China School of Business Jiangnan University Wuxi214122 China Faculty of Information Technology and Electrical Engineering University of Oulu Oulu90570 Finland VTT-Technology Research Center of Finland Kaitovayla 1 Oulu90570 Finland

The public release of ChatGPT represents a significant milestone in generative AI technology, enabling the autonomous generation of content based on pre-training. This breakthrough presents new opportunities for advancements in the field of portfolio selection. This paper aims to introduce a comprehensive portfolio selection method by applying ChatGPT for stock selection and combining it with optimization algorithms to jointly optimize portfolio selection. Compared to randomly selected stocks, the portfolios optimized using ChatGPT-selected stocks and solved with the egret swarm optimization algorithm (ESOA) demonstrate higher diversification and lower volatility, leading to superior portfolio optimization results. Additionally, to validate ESOA’s superiority, its performance is compared against genetic algorithm (GA) and particle swarm optimization (PSO) on five metrics: risk, expected return, Sharpe ratio, objective value, and penalty term. Under equivalent experimental setting, ESOA exhibits a better ability to balance the relationship between risk and return. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Optimization algorithms

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Optimal Deep Canonically Correlated Autoencoder-Enabled Prediction Model for Customer Churn Prediction

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computers, Materials & Continua 2022年第11期73卷 3757-3769页

作者： Olfat M.Mirza GJose Moses R.Rajender E.Laxmi Lydia Seifedine Kadry Cheadchai Me-Ead Orawit Thinnukool Department of Computer Science College of Computers and Information SystemsUmm Al-Qura UniversityMakkahSaudi Arabia Department of Computer Science Engineering Malla Reddy Engineering College500100India Department of Computer Science Engineering Lendi Institute of Engineering&TechnologyDenkada535005India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Applied Data Science Noroff University CollegeNorway Program in Statistics and Information Management Faculty of ScienceMaejo UniversityChiang Mai50290Thailand Department of Modern Management and Information Technology College of Arts Media and TechnologyChiang Mai UniversityChiang Mai50200Thailand

Presently,customer retention is essential for reducing customer churn in telecommunication *** churn prediction(CCP)is important to predict the possibility of customer retention in the quality of *** risks of customer churn also get essential,the rise of machine learning(ML)models can be employed to investigate the characteristics of customer ***,deep learning(DL)models help in prediction of the customer behavior based characteristic *** the DL models necessitate hyperparameter modelling and effort,the process is difficult for research communities and business *** this view,this study designs an optimal deep canonically correlated autoencoder based prediction(ODCCAEP)model for competitive customer dependent application *** addition,the O-DCCAEP method purposes for determining the churning nature of the *** O-DCCAEP technique encompasses preprocessing,classification,and hyperparameter ***,the DCCAE model is employed to classify the churners or ***,the hyperparameter optimization of the DCCAE technique occurs utilizing the deer hunting optimization algorithm(DHOA).The experimental evaluation of the O-DCCAEP technique is carried out against an own dataset and the outcomes highlighted the betterment of the presented O-DCCAEP approach on existing approaches.

关键词： Churn prediction customer retention deep learning machine learning archimedes optimization algorithm

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Study of IOT Enabled Water Logging Detection and Control in Drainage System for Smart City

Study of IOT Enabled Water Logging Detection and Control in ...

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2024 International Conference on Big Data Analytics in Bioinformatics, DABCon 2024

作者： Chakraborty, Subhalaxmi Sarkar, Ankan Das, Gourab Chatterjee, Shreya Kanjilal, Shamik Kundu, Pritam University of Engineering and Management Department of Cst & Csit Kolkata India University of Engineering and Management Kolkata Computer Science and Technology Kolkata India University of Engineering and Management Kolkata Computer Science and Information Technology Kolkata India Kolkata India

ISBN: (纸本)9798350378047

The Intelligent Urban Drainage System (IUDS) is a cutting-edge infrastructure designed for smart cities to efficiently manage stormwater runoff, wastewater, and flood control, utilizing the ESP32 controller board with Wi-Fi and Bluetooth connectivity to control and monitor the system. The Blynk server cloud platform enables remote monitoring and data analysis, providing real-time insights into system performance. The IUDS features a suite of sensors, and MQ2 natural gas and smoke detector sensor, which transmit data to the ESP32 board, activating the servo motor actuator to control drainage gates, valves, and pumps, ensuring optimal water flow, and minimizing flood risk. The automation control of the system and monitoring capabilities enable real-time water level monitoring and predictive maintenance, optimized drainage performance and reduced flood risk, enhanced water quality and environmental sustainability, and remote monitoring and data analysis through the Blynk server cloud platform, making the IUDS a resilient, efficient, and adaptable drainage system for smart cities, leveraging advanced technologies and IoT connectivity to create a sustainable and futuristic urban infrastructure. © 2024 IEEE.

关键词： Flood control

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Mitigating Racial Bias in Facial Expression Recognition Using Enhanced Data Diversification Techniques 15

Mitigating Racial Bias in Facial Expression Recognition Usin...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Sidana, Sagar Thakur, Sujata Negi Kumar, Sandeep Gomathi, R.D. Sinha, Anurag Saini, Arshroop Singh Raj, Petheru Dixit, Karan Kumar, Vikas Al-Khayyat, Ahmed Department of computer science and engineering Maharshi Dayanand University Haryana Rohtak India Department of computer science and engineering Graphic Era Hill University India Department of computer science and engineering Aryan Institute of Technology India Department of English Kongu Engineering College Perundurai Tamil Nadu Erode India School of Computing and information Science IGNOU India New Delhi India Secondary education Emerald Heights School India Reliance Jio Platforms Ltd Bangalore India Department of computer science and engineering Kiet Group of Institutions India Department of computer science and engineering Islamic University Iraq

ISBN: (纸本)9798350370249

With countless new methods and further advancements in computer vision, applying the technology to the everyday lives of people has become inevitable. One such use of computer vision models is recognizing facial patterns and expressions. However, as recent studies have indicated, many models interpreting facial expressions have developed biases towards certain races. This is likely a result of skewed training data;the model may have been trained with more images of a certain race than others. Overall, this can cause inaccuracy when the model is presented with people from different backgrounds. To promote a diverse group of data that the model can process, I present a way to balance biases. First, I fine-tuned a hybrid facial recognition model of state-of-the-art convolutional neural network (CNN) models called DeepFace on the emotion recognition dataset FER2013 to recognize the emotions on people’s faces. Next, using FairFace, which is modeled after the large-scale CNN ResNet-34, I extrapolated the races and facial structures for each type of individual, which I then used to determine the bias and skew within the dataset. I did this by comparing the model’s accuracy when applied to different races. Then, I used a user-instructed stable diffusion model called InstructPix2Pix to modify the images and create a more well-represented and diverse dataset. With this method, I was able to balance the dataset effectively and reduce bias in the image recognition model. However, due to issues with the FER2013 dataset images, many of the transformed images were disfigured, meaning the emotion data within the images was lost. This caused the overall accuracy of the model to drop significantly. Future research can evaluate this method using InstructPix2Pix on different computer vision tasks or with different datasets. © 2024 IEEE.

关键词： Convolutional neural networks

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Navigating Threats: A Vulnerability Analysis of TCAS Interaction with Other Aircraft Systems 43

Navigating Threats: A Vulnerability Analysis of TCAS Interac...

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43rd AIAA DATC/IEEE Digital Avionics Systems Conference, DASC 2024

作者： Kuba, Sakurako Babiceanu, Radu F. Embry-Riddle Aeronautical University Department of Electrical Engineering and Computer Science Daytona BeachFL United States

ISBN: (纸本)9798350349610

With the increasing integration of automated systems in modern aircraft, the security of avionics systems has become a critical concern. This research aims to conduct a vulnerability analysis of TCAS-related systems, protocols, attack surfaces, scenarios, and operational impacts to identify attack vectors and entry points to threat flight safety. Mapping out a comprehensive systematic architecture diagram enables the identification of interdependencies and data flow among TCAS, aircraft control, and communication systems. The architecture diagram assists in identifying entry points that could exploit vulnerabilities in one system to compromise the security or integrity of other interconnected systems. Understanding the TCAS-related system dependencies is also significant to develop attack scenarios intentionally exploited by malicious actors. This analysis evaluates the severity of impacts and likelihood of exploitation for each scenario to evaluate risk levels. This new approach of vulnerability analysis and risk assessment for TCAS-relevant systems would support developing countermeasures to enhance the security posture of avionics connectivity in future implementations. This analysis emphasizes that human response is likely to be targeted and it could potentially result in a catastrophic outcome in terms of flight safety. This approach will contribute to the development of proactive strategies for mitigating risks and enhancing flight safety against emerging threats in the modern dynamic airspace environment. © 2024 IEEE.

关键词： Aircraft accidents

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Physics-Informed Convolutional Autoencoder for Cyber Anomaly Detection in Power Distribution Grids

Physics-Informed Convolutional Autoencoder for Cyber Anomaly...

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2024 IEEE Power and Energy Society General Meeting, PESGM 2024

作者： Zideh, Mehdi Jabbari Solanki, Sarika Khushalani West Virginia University Lane Department of Computer Science and Electrical Engineering MorgantownWV26505 United States

The growing trend toward the modernization of power distribution systems has facilitated the installation of advanced measurement units and promotion of the cyber communication systems. However, these infrastructures are still prone to stealth cyber attacks. The existing data-driven anomaly detection methods suffer from a lack of knowledge about the system's physics, lack of interpretability, and scalability issues hindering their practical applications in real-world scenarios. To address these concerns, physics-informed neural networks (PINNs) were introduced. This paper proposes a multivariate physics-informed convolutional autoencoder (PIConvAE) to detect stealthy cyber-attacks in power distribution grids. The proposed model integrates the physical principles into the loss function of the neural network by applying Kirchhoff's law. Simulations are performed on the modified IEEE 13-bus and 123-bus systems using OpenDSS software to validate the efficacy of the proposed model for stealth attacks. The numerical results prove the superior performance of the proposed PIConvAE in three aspects: a) it provides more accurate results compared to the data-driven ConvAE model, b) it requires less training time to converge c) the model excels in effectively detecting a wide range of attack magnitudes making it powerful in detecting stealth attacks. © 2024 IEEE.

关键词： Convolution

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FAST NONLINEAR VECTOR QUANTILE REGRESSION 11

FAST NONLINEAR VECTOR QUANTILE REGRESSION

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11th International Conference on Learning Representations, ICLR 2023

作者： Rosenberg, Aviv A. Vedula, Sanketh Romano, Yaniv Bronstein, Alex M. Department of Computer Science Technion Israel Department of Electrical and Computer Engineering Technion Israel Sibylla United Kingdom

Quantile regression (QR) is a powerful tool for estimating one or more conditional quantiles of a target variable Y given explanatory features X.A limitation of QR is that it is only defined for scalar target variables, due to the formulation of its objective function, and since the notion of quantiles has no standard definition for multivariate ***, vector quantile regression (VQR) was proposed as an extension of QR for vector-valued target variables, thanks to a meaningful generalization of the notion of quantiles to multivariate distributions via optimal *** its elegance, VQR is arguably not applicable in practice due to several limitations: (i) it assumes a linear model for the quantiles of the target Y given the features X;(ii) its exact formulation is intractable even for modestly-sized problems in terms of target dimensions, number of regressed quantile levels, or number of features, and its relaxed dual formulation may violate the monotonicity of the estimated quantiles;(iii) no fast or scalable solvers for VQR currently *** this work we fully address these limitations, namely: (i) We extend VQR to the non-linear case, showing substantial improvement over linear VQR;(ii) We propose vector monotone rearrangement, a method which ensures the quantile functions estimated by VQR are monotone functions;(iii) We provide fast, GPU-accelerated solvers for linear and nonlinear VQR which maintain a fixed memory footprint, and demonstrate that they scale to millions of samples and thousands of quantile levels;(iv) We release an optimized python package of our solvers as to widespread the use of VQR in real-world applications. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Vectors

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A Novel Approach for Human Activity Recognition Using Vision Based Method 1

A Novel Approach for Human Activity Recognition Using Vision...

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1st International Conference on Circuits, Power, and Intelligent Systems, CCPIS 2023

作者： Parida, Lomashradhha Parida, Bivasa Ranjan Mishra, Manas Ranjan Jayasingh, Suvendra Kumar Samal, Tusharkanta Ray, Sujata Institute of Management and Information Technology Department of Computer Science and Engineering Cuttack India Silicon Institute of Technology Department of Computer Science and Engineering Bhubaneswar India C. V. Raman Global University Department of Computer Science and Engineering Bhubaneswar India Dhaneswar Rath Institute of Engineering and Management Studies Department of Computer Science and Engineering Cuttack India

ISBN: (纸本)9798350315769

Human Activity Recognition (HAR) is a fascinating process that involves identifying and categorizing human activities based on observations of subject behavior and environmental factors. Out of the major phases of HAR, preprocessing and feature extraction, in particular, require a lot of attention because they constitute the foundation for the training phase. The current evaluation effectively examines the trends in vision-based research approaches. Many models based on the artificial intelligence are suggested for the activity recognition;however, they couldn't perform satisfactorily on real world long-term HAR due to their lacuna in the extraction of spatial and temporal data. In light of these drawbacks, we propose a hybrid methodology for the human activity recognition which integrates Convolutional Neural Network (CNN) with the Long Short-Term Memory (LSTM), where CNN works for extracting the spatial characteristics and LSTM is used for the learning of the temporal information. A complete evaluation of the substantial work done in HAR domain and its vision-based techniques would help the readers. © 2023 IEEE.

关键词： Long short-term memory

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