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arXiv 2024年

作者： Gakhar, Ishaan Guha, Aryesh Gupta, Aryaman Agarwal, Amit Toshniwal, Durga Verma, Ujjwal Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education India Department of Electrical and Electronics Engineering Manipal Institute of Technology Manipal Academy of Higher Education India Department of Computer Science Engineering Manipal Institute of Technology Manipal Academy of Higher Education India Enterprise Analytics & Data Science Artificial Intelligence Center of Excellence Wells Fargo International Solutions Private Limited Center For Transportation Systems Indian Institute of Technology Roorkee India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education India

The scarcity of comprehensive datasets in the traffic light detection and recognition domain and the poor performance of state-of-the-art models under hostile weather conditions present significant challenges. To address these issues, this paper proposes a novel approach by merging two widely used datasets, LISA and S2TLD. The merged dataset is further processed to tackle class imbalance, a common problem in this domain. This merged dataset becomes our source domain. Synthetic rain and fog are added to the dataset to create our target domain. We employ Fourier Domain Adaptation (FDA) to create a final dataset with a minimized domain gap between the two datasets, helping the model trained on this final dataset adapt to rainy and foggy weather conditions. Additionally, we explore Semi-Supervised Learning (SSL) techniques to leverage the available data more effectively. Experimental results demonstrate that models trained on FDA-augmented images outperform those trained without FDA across confidence-dependent and independent metrics, like mAP50, mAP50-95, Precision, and Recall. The best-performing model, YOLOv8, achieved a Precision increase of 5.1860%, Recall increase of 14.8009%, mAP50 increase of 9.5074%, and mAP50- 95 increase of 19.5035%. On average, percentage increases of 7.6892% in Precision, 19.9069% in Recall, 15.8506% in mAP50, and 23.8099% in mAP50-95 were observed across all models, highlighting the effectiveness of FDA in mitigating the impact of adverse weather conditions on model performance. These improvements pave the way for real-world applications where reliable performance in challenging environmental conditions is critical. Impact Statement-This research addresses a critical challenge in autonomous driving systems-poor performance in adverse weather conditions-by introducing a novel method using Fourier Domain Adaptation (FDA). The FDA-enhanced models significantly improve the detection of traffic lights in rain and fog, which has broad implicatio

关键词： Rain

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Another estimation of Laplacian spectrum of the Kronecker product of graphs

arXiv

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arXiv 2021年

作者： Bašić, Milan Arsić, Branko Obradović, Zoran Department of Computer Science University of Niš Serbia Department of Mathematics and Informatics University of Kragujevac Serbia Department of Computer and Information Sciences Center for Data Analytics and Biomedical Informatics Temple University PhiladelphiaPA United States

The relationships between eigenvalues and eigenvectors of a product graph and those of its factor graphs have been known for the standard products, while characterization of Laplacian eigenvalues and eigenvectors of the Kronecker product of graphs using the Laplacian spectra and eigenvectors of the factors turned out to be quite challenging and has remained an open problem to date. Several approaches for the estimation of Laplacian spectrum of the Kronecker product of graphs have been proposed in recent years. However, it turns out that not all the methods are practical to apply in network science models, particularly in the context of multilayer networks. Here we develop a practical and computationally efficient method to estimate Laplacian spectra of this graph product from spectral properties of their factor graphs which is more stable than the alternatives proposed in the literature. We emphasize that a median of the percentage errors of our estimated Laplacian spectrum almost coincides with the x-axis, unlike the alternatives which have sudden jumps at the beginning followed by a gradual decrease for the percentage errors. The percentage errors confined (confidence of the estimations) up to ±10% for all considered approximations, depending on a graph density. Moreover, we theoretically prove that the percentage errors becomes smaller when the network grows or the edge density level increases. Additionally, some novel theoretical results considering the exact formulas and lower bounds related to the certain correlation coefficients corresponding to the estimated eigenvectors are presented. © 2021, CC0.

关键词： Eigenvalues and eigenfunctions

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Enhancing drug and cell line representations via contrastive learning for improved anti-cancer drug prioritization

arXiv

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arXiv 2023年

作者： Lawrence, Patrick J. Ning, Xia Biomedical Informatics Department The Ohio State University 1800 Cannon Drive Lincoln Tower 250 ColumbusOH43210 United States Computer Science and Engineering Department The Ohio State University 2015 Neil Avenue ColumbusOH43210 United States Translational Data Analytics Institute The Ohio State University 1760 Neil Avenue ColumbusOH43210 United States

Due to cancer’s complex nature and variable response to therapy, precision oncology informed by omics sequence analysis has become the current standard of care. However, the amount of data produced for each patients makes it difficult to quickly identify the best treatment regimen. Moreover, limited data availability has hindered computational methods’ abilities to learn patterns associated with effective drug-cell line pairs. In this work, we propose the use of contrastive learning to improve learned drug and cell line representations by preserving relationship structures associated with drug mechanism of action and cell line cancer types. In addition to achieving enhanced performance relative to a state-of-the-art method, we find that classifiers using our learned representations exhibit a more balances reliance on drug- and cell line-derived features when making predictions. This facilitates more personalized drug prioritizations that are informed by signals related to drug resistance. © 2023, CC BY.

关键词： Cell culture

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An Efficient Ensemble Model for Forecasting Time Series Analysis of Crypto Currency data Using Machine Learning

An Efficient Ensemble Model for Forecasting Time Series Anal...

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2021 IEEE International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2021

作者： Saravanakumar, C. Lakshmi, S. Kanaga Prakash, M. Vijayakumar, M. Arun, C. St. Joseph's Institute of Technology Department of Information Technology Chennai India Sri Muthukumaran Institute of Technology Department of Management Studies Chennai India Data Science and Analytics Center Karpagam College of Engineering Coimbatore India St. Joseph's Institute of Technology Department of Computer Science and Engineering Chennai India R.M.K College of Engineering and Technology Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9781665435215

Time series data are analyzed for predicting the future based on the consistent time period using statistical methods. These analyses are based on the data which are extracted from different sources. Existing method for performing the forecasting process does not fit into the crypto currency data. GARCH and ARIMA models with intelligent mechanisms provide the solution but it is not at the proper level. RNN model takes the data from preexisting intervals and decides the next time series data which is represented as a directed graph. Two types of behavior are considered namely spatial and temporal. The linear model uses the homogeneous data and heterogeneous data analysis is based on the nonlinear model. The nodes and layers are connected based on the input and corresponding activation unit. The memory model is attached to the RNN model for making persistent storage for keeping the processed data for a longer time. The proposed model uses the ensemble method for integrating various RNN models for achieving a high level of accuracy. © 2021 IEEE.

关键词： Time series analysis

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Study of deep learning techniques for medical image analysis: A review

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Materials Today: Proceedings 2022年 56卷 209-214页

作者： Ayush Singhal Manu Phogat Deepak Kumar Ajay Kumar Mamta Dahiya Virendra Kumar Shrivastava Department of Computer Science Engineering Faculty of Engineering and Technology Shree Guru Gobind Singh Tricentenary University Gurugram 122505 Haryana India Department of Mechanical Engineering Faculty of Engineering & Technology Shree Guru Gobind Singh Tricentenary University Gurugram 122505 Haryana India Department of Big Data Analytics Adani Institute of Digital Technology Management Gandhinagar Gujarat 382423 India

Now, there is a lot of research going on in the field of medical image analysis by using deep convolutional networks. Deep learning uses various models to extract the information from the images provided to deep learning model. The deep learning is now widely used in the field of medical to detect and diagnose the disease and after diagnosing classifying it into particular category of the disease. The most widely model used for medical image analysis is Convolutional neural network. So, this review paper focusses on how deep learning uses deep networks to detect the disease by retrieving or extracting the information from the images provided to the network and also give information about the clinical applications in the medical fields and the limitations of deep learning in image analysis process is also highlighted.

关键词： Deep learning Medical image analysis Convolutional neural network

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Urban Sensing Based on Mobile Phone data:Approaches,Applications,and Challenges

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IEEE/CAA Journal of Automatica Sinica 2020年第3期7卷 627-637页

作者： Mohammadhossein Ghahramani MengChu Zhou Gang Wang Insight Centre for Data Analytics University College DublinDublin 4Ireland IEEE Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyMacao 999078China Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China China Telecom at Macao Macao 999078China

data volume grows explosively with the proliferation of powerful smartphones and innovative mobile *** ability to accurately and extensively monitor and analyze these data is *** concern in cellular data analysis is related to human beings and their *** to the potential value that lies behind these massive data,there have been different proposed approaches for understanding corresponding *** that end,analyzing people's activities,e.g.,counting them at fixed locations and tracking them by generating origindestination matrices is *** former can be used to determine the utilization of assets like roads and city *** latter is valuable when planning transport *** insights allow a government to predict the adoption of new roads,new public transport routes,modification of existing infrastructure,and detection of congestion zones,resulting in more efficient designs and *** data exploration can help research in various fields,e.g.,urban planning,transportation,health care,and business *** can also help organizations in decision making,policy implementation,monitoring,and evaluation at all *** work aims to review the methods and techniques that have been implemented to discover knowledge from mobile phone *** classify these existing methods and present a taxonomy of the related work by discussing their pros and cons.

关键词： Big data analysis human mobility origin destination matrices smart infrastructure urban planning

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Awareness requirement and performance management for adaptive systems: a survey

arXiv

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arXiv 2023年

作者： Rashid, Tarik A. Hassan, Bryar A. Alsadoon, Abeer Qader, Shko Vimal, S. Chhabra, Amit Yaseen, Zaher Mundher Computer Science and Engineering Department School of Science and Engineering University of Kurdistan Hewler Kurdistan Region Erbil Iraq Department of Information Technology Kurdistan Institution for Strategic Studies and Scientifc Research KRI Sulaymaniyah46001 Iraq School of Computing and Mathematics Charles Sturt University Sydney Australia School of Computing Engineering and Mathematics Western Sydney University Sydney City Campus Sydney Australia Sydney Australia Information Technology Department Kent Institute Australia Sydney Australia Department of Information Technology University College of Goizha Kurdistan Region Sulaimani Iraq Data Analytics Lab Department of Artifcial Intelligence and Data Science Ramco Institute of Technology North Venganallur Village Virudhunagar District Tamilnadu Rajapalayam626 117 India Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar India Civil and Environmental Engineering Department King Fahd University of Petroleum & Minerals Dhahran31621 Saudi Arabia Department of Computer Science College of Science Charmo University KRI Chamchamal46023 Iraq Department of Information Technology Computer Science Institute Sulaimani Polytechnic University KRI Sulaymaniyah46001 Iraq

Self-adaptive software can assess and modify its behavior when the assessment indicates that the program is not performing as intended or when improved functionality or performance is available. Since the mid-1960s, the subject of system adaptivity has been extensively researched, and during the last decade, many application areas and technologies involving self-adaptation have gained prominence. All of these efforts have in common the introduction of self-adaptability through software. Thus, it is essential to investigate systematic software engineering methods to create self-adaptive systems that may be used across different domains. The primary objective of this research is to summarize current advances in awareness requirements for adaptive strategies based on an examination of state-of-the-art methods described in the literature. This paper presents a review of self-adaptive systems in the context of requirement awareness and summarizes the most common methodologies applied. At first glance, it gives a review of the previous surveys and works about self-adaptive systems. Afterward, it classifies the current self-adaptive systems based on six criteria. Then, it presents and evaluates the most common self-adaptive approaches. Lastly, an evaluation among the self-adaptive models is conducted based on four concepts (requirements description, monitoring, relationship, dependency/impact, and tools). © 2023, CC BY-NC-ND.

关键词： Adaptive systems

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Unleashing The Power Of 6g Assisted Wireless Communication Network To Define A New Routing Strategy To Preserve Communication Energy

Unleashing The Power Of 6g Assisted Wireless Communication N...

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International Conference on Electrical Energy Systems (ICEES)

作者： S. Avinash S. Sujatha D. Poornima Vedhashree S.K V. Saravanan P. Malathi Department of MBA St. Josephs College of Engineering Chennai Tamil Nadu India Department of EEE Sri Sairam College of Engineering Anekal Bangalore Karnataka Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Department of Electronics and Communication Engineering M.Kumarasamy College of Engineering Chennai Tamil Nadu India Department of Nano Electronics Materials and Sensors Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu India Department of Data Analytics Saveetha College of Liberal Arts and Sciences SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798350353778

ISBN: (纸本)9798350353785

The incorporation of cutting-edge technology into intelligent power grid management becomes a top priority as the globe keeps searching for effective and environmentally friendly energy solutions. The arrival of 6G wireless networks is expected to completely transform the methods used to track, manage, and enhance energy grids. An effort to provide high data speeds has already been made with the deployment of commercial fifth-generation (5G) wireless communications networks. The advent of IoT applications and the proliferation of smart devices will put a heavy strain on 5G wireless networks since they need extremely dependable and lowlatency connection. So, the massive continuous data traffic boom is probably going to be too much for 5G networks to handle. Research into enhancing current wireless networks in preparation for the 6G cellular systems, the next generation of mobile communication, has been spurred by this. This paper introduces the Neural 6G Wireless Routing Protocol (N6GWRP), a new routing protocol that supports intelligent communication. To evaluate the effectiveness of the suggested approach, it is cross-validated with the Multiple-Access Routing Protocol (MARP), a traditional routing model. Users should not experience any disruptions when mobile power and data transmission systems interact. Another major obstacle is connecting the vast array of devices and people with varying degrees of processing power in a way that is both sustainable and environmentally conscious. This study delves into the smart solution to the aforesaid difficulties and examines how 6G wireless networks might improve smart energy grid resilience and dependability through seamless interaction with other sophisticated technologies like blockchain. A more environmentally friendly and smart vision regarding electrical grid administration is possible thanks to the extensive study that strives to illuminate the revolutionary significance of 6G wireless technologies.

关键词： 6G mobile communication 5G mobile communication Wireless networks Transforms Routing Routing protocols Smart grids Sustainable development Smart devices Strain

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Novel Antenna Designs for High data Rate Body-Centric Communications in Wearable Devices

Novel Antenna Designs for High Data Rate Body-Centric Commun...

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Automation and Computation (AUTOCOM), International Conference on

作者： Ishika Soni Manjunatha R V Vijaya Baskar Daljeet Pal Singh Komal Parashar Niranjan Panda Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India School of Sciences Data analytics and Mathematical Sciences JAIN (Deemed to be University) Bangalore Karnataka India Department of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

There is an ever-increasing demand for high data rate communication embedded in wearable devices owing to the exponential growth of smart devices and related technology. The human body is always around, which means that traditional antenna designs will not work well with body-centric communications operations, as signal loss and interference from the presence of the human body must be addressed. This project addresses this limitation by designing new body-centric communications antenna designs for incorporation into wearable devices. Antennas proposed in the literature can use novel techniques that reach high data rates while providing reliable communication links and features like compactness, low power consumption, and multi-band functionalities. The designs are optimized to ensure that the human body has a minimal effect on the signal enhancing performance and stability. The antennas are designed and simulated using advanced simulation tools and validated in the lab. These findings demonstrate the remarkable advantages of high-speed data transmission and link quality compared to traditional antennas. It contributes to developing novel antenna designs that wearable device manufacturers can incorporate into high data rate body-centric communications, enabling dramatically improved performance within tight space constraints. This will make it possible to create next-generation wearable devices with communication functions for applications ranging from healthcare and sports to virtual reality.

关键词： Performance evaluation Training Design methodology Transmitting antennas Clothing Virtual reality Stability analysis Reliability Wearable devices Antennas

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Adaptive Estimation of Graphical Models under Total Positivity

arXiv

引用

arXiv 2022年

作者： Ying, Jiaxi de M. Cardoso, José Vinícius Palomar, Daniel P. Department of Mathematics The Hong Kong University of Science and Technology Clear Water Bay Hong Kong HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute Shenzhen China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong Department of Electronic and Computer Engineering Department of Industrial Engineering and Data Analytics The Hong Kong University of Science and Technology Clear Water Bay Hong Kong

We consider the problem of estimating (diagonally dominant) M-matrices as precision matrices in Gaussian graphical models. These models exhibit intriguing properties, such as the existence of the maximum likelihood estimator with merely two observations for M-matrices [Lauritzen et al., 2019;Slawski & Hein, 2015] and even one observation for diagonally dominant M-matrices [Truell et al., 2021]. We propose an adaptive multiple-stage estimation method that refines the estimate by solving a weighted 1-regularized problem at each stage. Furthermore, we develop a unified framework based on the gradient projection method to solve the regularized problem, incorporating distinct projections to handle the constraints of M-matrices and diagonally dominant M-matrices. A theoretical analysis of the estimation error is provided. Our method outperforms state-of-the-art methods in precision matrix estimation and graph edge identification, as evidenced by synthetic and financial time-series data sets. Copyright © 2022, The Authors. All rights reserved.

关键词： Graphic methods

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