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Emerging Technologies and Innovation for Sustainability (EmergIN), International Conference on

作者： P. Nandhini Kaviyaraj R B. Seetha Lakshimi K Ayyappan Saravanan Obuli Department of Computer Science and Engineering Sri Shanmugha College of Engineering and Technology Salem India Department of Computational Intelligence SRM Institute of Science and Technology Kattankulathur India AI & DS Sri Shanmugha College of Engineering and Technology Salem India Department of Computer Science and Engineering Loyala Institute of Technology Chennai India Department of Computer Science and Engineering Annapoorna Engineering College Salem India

ISBN: (数字)9798350391268

ISBN: (纸本)9798350391275

To address complexity of cancer progression, we propose a novel approach using 3D data visualization with brain tumors, the leading cause of cancer related death in both children and adults worldwide. Standard methods for studying cancer development produce incomplete or oversimplified models that can't capture the complex dynamics of tumor cells. In contrast, advanced data science and a new generation of 3D visualization technologies simultaneously bring about the opportunity to explore the dynamical landscape of tumorous cells. With 3D visualization tools we create high level illustrations that go beyond basic renderings and deliver more sophisticated, actionable insights that take advantage of analysis of advanced data analytics and feature analysis at the cluster and genomic level. A new methodology is introduced in this paper, based the use of the Unity game development platform, that enables transitions smoothly from 2D to 3D to better describe the various stages of cancer progression. Furthermore, we propose an innovative way for the reconstruction of 3D models right in Unity using the AR platforms such as the specialized augmented reality AR SDK toolkit Vuforia. Not only does our approach extend current understanding of brain tumor dynamics, but more importantly, gives a flexible framework to see tumor behaviour with real time visualization that could substantially change therapeutic development. Ultimately, this work can serve as a stepping stone for such integration of immersive visualization techniques into clinical practice and to ultimately facilitate more effective, personalized treatments and precision medicine approaches.

关键词： Three-dimensional displays Data visualization Brain tumors Surgery Medical services Brain modeling Planning Augmented reality Standards Cancer

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Graph Convolutional Network with Neural Inductive Matrix Completion for Predicting Disease-Related LncRNA Genes

Graph Convolutional Network with Neural Inductive Matrix Com...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Yang, Qiang Zhang, Shuxin Dong, Suyu Xu, Long Dong, Weihe Li, Xiaokun Sun, Pengzhong Jiang, Feng Zhang, Xianyu Luo, Gongning Northeast Forestry University College of Computer and Control Engineering Harbin China Harbin Institute of Technology School of Medicine and Health Harbin China Heilongjiang University School of Computer Science and Technology Harbin China Harbin Institute of Technology School of Computer Science and Technology Harbin China Harbin Medical University Cancer Hospital Department of Breast Surgery Harbin China King Abdullah University of Science and Technology Computer Electrical and Mathematical Sciences & Engineering Division Thuwal Saudi Arabia

ISBN: (纸本)9798350337488

Numerous researches emphasized that long non-coding RNA (lncRNA) plays a vital factor in various biological processes, and its mismatched expression and dysfunction are tightly linked with the occurrence of human diseases. Thus, computational models were designed to identify lncRNA-disease interactions by merging heterogeneous biological data. However, most of them neglected the intrinsic structure of multi-source information, which limits the performance for potential lncRNA-disease association prediction. Here, GCN-NIMC is introduced to alleviate the dilemma for disease-associated lncRNA genes identification based on the graph convolutional network with neural inductive matrix. This method builds a feature matrix with multi-source heterogeneous data and then learn the various information contained in the feature matrix for the sake of acquiring better feature expressions of the lncRNA-disease interactions. Experimental results on 10-repeated 5-fold cross-validation demonstrated that our proposed GCN-NIMC is superior to existing cutting-edge methods for identifying disease-related lncRNA genes. Furthermore, case studies confirmed our computational method as a practical tool with clinical benefits to develop the therapeutic schedule at lncRNA-level. © 2023 IEEE.

关键词： gene identification graph convolutional network inductive matrix completion lncRNA-disease association

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A 5G NB-IoT Infrastructure for Secured Demand-Side Information Transmission and Predictive Analyses in Smarter Grids 3

A 5G NB-IoT Infrastructure for Secured Demand-Side Informati...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Ramana, K Seshadri Priyadarshini, Y. Indira Jambukesh, H.J. Singh, Rajesh Kandasamy, Manivel Kumar, Bura Vijay Ravindra College of Engineering for Women Department of Computer Science and Engineering Kurnool India Ravindra College of Engineering for Women Department of CSE Kurnool India Government Engineering College Electronics and Communication Haveri India Uttaranchal University Uttaranchal Institute of Technology India Karnavati University Unitedworld School of Computational Intelligence Gujarat Gandhinagar382422 India SR University School of Computer Science and Artificial Intelligence Telangana Warangal India

ISBN: (纸本)9798350399264

In sequence to start making utilization of Dedicated Short - spectrum of devices (NB-IoT) just on buyer edge of the energy grid, improve the security of transferring details, and utilize market records, we focus on the dependable interaction, trustworthy assistance, and interesting analyzation of 5G + Smart Grid as well as we construct a conclusive remedy that includes encrypting data stations, managed service providers, and clever analytical strategies. The proposed 5GNBIoT design from the research can aid in grid control and market responses in contrast to increasing the degree of connectivity among Five G and the nationwide grid. This study will therefore help bring a new wave of electronic innovations to the modern grid, develop a deeper integrated services or software for the 5G + Modern Wire, improve the networks' cognitive, and advance the dual carbon goal of the energy network. © 2023 IEEE.

关键词： Commerce

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Analysis of Banking and Financial Sector Services Using Artificial Intelligence (AI) 1

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8th International Conference on Information System Design and Intelligent Applications, ISDIA 2024

作者： Isavnin, Alexey Kosorukova, Irina Madaminov, Inomjon Laxmi Lydia, E. Satapathy, Suresh Department of Business-Informatics and Mathematical Methods in Economics Naberezhnye Chelny Institute KFU Kazan Federal University Naberezhnye Chelny Russia Department of Corporate Finance and Corporate Governance Financial University under the Government of the Russian Federation Moscow Russia Department of Accounting and Auditing Urgench State University Urgench Uzbekistan Department of Computer Science and Engineering Vignan’s Institute of Information Technology Visakhapatnam530049 India School of Computer Engineering KIIT Deemed to be University Bhubaneswar India

ISBN: (数字)9789819748921

ISBN: (纸本)9789819748914

This research proposes the assessment of banking and financial services using AI to monitor how banks apply AI approaches and the feedback they receive from customers. In order to better track, anticipate, and react to customer behavior, the financial information generated by the growing utilization of digital pay and banking can be mined by banks and other financial institutions. Based on research, secondary sources are reviewed to ascertain the data used in banking and financial services. A methodical survey is designed to gather the main information clients have regarding AI applications. The study is that private banks and other financial institutions use a variety of artificial intelligence (AI) services to better serve their clients and ensure that they are happy with their financial services, as some clients are not happy with banking services. Additionally, the results indicated that clients would be more committed to banking and financial services representatives by providing creative preparation for the advancement of AI practices in the workplace. © The Author(s).

关键词： Decentralized finance

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Optimized Predictive Storage Resource Allocation Through Neural Forecasting for Cloud Environments

Optimized Predictive Storage Resource Allocation Through Neu...

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2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Sridevi Ponmalar, P. Adithya Pothan Raj, V. Jebaseelan, S. D. Sundarsingh Phade, Gayatri Srithar, V. Harikrishnan, R. School of computing Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur Campus Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Dept. of Cse Chennai India Sathyabama Institute of Science and Technology Dept of Eee Chennai119 India Sandip Institute of Technology & Research Centre Nashik India PeriyarManiammai Institute of Science & Technology Department of Computer Applications Thanjavur India Symbiosis Institute of Technology Symbiosis International Deemed University Pune Campus Pune412115 India

ISBN: (纸本)9798331543617

The demand for efficient and scalable management of storage resources is growing enormously with the rapid growth in data-intensive applications within the cloud environment. Optimized PSRANF is the state-of-the-art approach towards dynamic storage resource allocation, based on the forecast of future demands in storage. PSRANF utilizes state-of-the-art neural forecasting with Long Short-Term Memory (LSTM) networks and Transformer designs to analyze the pattern of historical storage usage for highly accurate future need predictions. By proactively adjusting storage capacity to these predictions, PSRANF strikes an optimal balance between performance and cost by reserving just-in-time resources. It ensures that there is no risk of over-provisioning, which would be too costly. In the same breath, the risk of under-provisioning, which leads to performance bottlenecks, is avoided. What is more, the system will apply prioritization, placing the data of higher priority or frequently accessed data into faster storage tiers of a higher class of performance and less important classes of data into cost-effective storage. The adaptive feedback loop continuously refines the forecasting model, which, over time, improves accuracy and allows the system to handle changes in workload patterns. PSRANF combines neural forecasting with resource allocation in such a way that is cost-effective;thus, it is very well placed for cloud environments where the demand for storage is dynamic in nature and requires intelligent data-driven approaches. The AWS implementation of PSRANF has turned out to be quite a helpful tool in optimizing the storage resource allocation process. While it is able to predict 98.5% of the storage needs correctly by achieving a cost saving of up to 18.7%, managing data from 97.8% to 99.2%, and resources from 95.4%, it's robust for improvements in cloud storage management and operational efficiency. © 2024 IEEE.

关键词： Storage efficiency

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On the Coverage Required for Diploid Genome Assembly

arXiv

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arXiv 2024年

作者： Mahajan, Daanish Jain, Chirag Kashyap, Navin Department of Interdisciplinary Mathematical Sciences Indian Institute of Science Bangalore560012 India Department of Computational and Data Sciences Indian Institute of Science Bangalore560012 India Department of Electrical Communication Engineering Indian Institute of Science Bangalore560012 India

Repeat content and heterozygosity rate of the target genome are crucial factors in determining the ability to generate a complete telomere-to-telomere assembly. The mathematical relationship between the required coverage and read length for the purpose of unique reconstruction remains unexplored for diploid genomes. We investigate the information-theoretic conditions that the given set of sequencing reads must satisfy to achieve the complete reconstruction of the true sequence of a diploid genome. We also analyze the standard greedy and de-Bruijn graph-based assembly algorithms. Our results show that the coverage and read length requirements of the assembly algorithms are considerably higher than the lower bound because both algorithms require the double repeats in the genome to be bridged. Finally, we derive the necessary conditions for the overlap graph-based assembly paradigm. © 2024, CC BY-NC-ND.

关键词： Genome

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Strong nonlinear optical processes with extraordinary polarization anisotropy in inversionsymmetry broken two-dimensional PdPSe

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Light(science & Applications) 2024年第8期13卷 1524-1533页

作者： Song Zhu Ruihuan Duan Xiaodong Xu Fangyuan Sun Wenduo Chen Fakun Wang Siyuan Li Ming Ye Xin Zhou Jinluo Cheng Yao Wu Houkun Liang Junichiro Kono Xingji Li Zheng Liu Qi Jie Wang School of Electrical and Electronic Engineering Nanyang Technological University639798 SingaporeSingapore School of Material Science and Engineering Nanyang Technological University639798 SingaporeSingapore CINTRA CNRS/NTU/THALES UMI 3288Research Techno PlazaNanyang Technological University637371 SingaporeSingapore School of Materials Science and Engineering Harbin Institute of Technology150001 HarbinChina Department of Chemistry National University of Singapore117543 SingaporeSingapore GPL Photonics Lab State Key Laboratory of Applied OpticsChangchun Institute of OpticsFine Mechanics and PhysicsChinese Academy of Sciences130033 ChangchunChina School of Electronics and Information Engineering Sichuan University610064 ChengduSichuanChina School of Physical and Mathematical Sciences Nanyang Technological University637371 SingaporeSingapore Departments of Electrical and Computer Engineering Physics and Astronomyand Materials Science and NanoEngineeringRice UniversityHoustonTXUSA

Nonlinear optical activities,especially second harmonic generation(SHG),are key phenomena in inversion-symmetrybroken two-dimensional(2D)transition metal dichalcogenides(TMDCs).On the other hand,anisotropic nonlinear optical processes are important for unique applications in nano-nonlinear photonic devices with polarization functions,having become one of focused research topics in the field of nonlinear ***,the strong nonlinearity and strong optical anisotropy do not exist simultaneously in common 2D ***,we demonstrate strong secondorder and third-order susceptibilities of 64 pm/V and 6.2×10^(-19)m^(2)/V^(2),respectively,in the even-layer PdPSe,which has not been discovered in other common TMDCs(e.g.,MoS2).Strikingly,it also simultaneously exhibited strong SHG anisotropy with an anisotropic ratio of~45,which is the largest reported among all 2D materials to date,to the best of our *** addition,the SHG anisotropy ratio can be harnessed from 0.12 to 45(375 times)by varying the excitation wavelength due to the dispersion ofχ^(2)*** an illustrative example,we further demonstrate polarized SHG imaging for potential applications in crystal orientation identification and polarization-dependent spatial *** findings in 2D PdPSe are promising for nonlinear nanophotonic and optoelectronic applications.

关键词： materials anisotropy nonlinear

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New Combinatorial Insights for Monotone Apportionment

arXiv

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arXiv 2024年

作者： Cembrano, Javier Correa, José Schmidt-Kraepelin, Ulrike Tsigonias-Dimitriadis, Alexandros Verdugo, Victor Department of Algorithms and Complexity Max Planck Institute for Informatics Germany Department of Industrial Engineering Universidad de Chile Chile Department of Mathematics and Computer Science TU Eindhoven Netherlands European Central Bank Germany Institute for Mathematical and Computational Engineering Department of Industrial and Systems Engineering PUC Chile Chile

The apportionment problem constitutes a fundamental problem in democratic societies: How to distribute a fixed number of seats among a set of states in proportion to the states’ populations? This—seemingly simple—task has led to a rich literature and has become well known in the context of the US House of Representatives. In this paper, we connect the design of monotone apportionment methods to classic problems from discrete geometry and combinatorial optimization and explore the extent to which randomization can enhance proportionality. We first focus on the well-studied family of stationary divisor methods, which satisfy the strong population monotonicity property, and show that this family produces only a slightly superlinear number of different outputs as a function of the number of states. While our upper and lower bounds leave a small gap, we show that—surprisingly—closing this gap would solve a long-standing open problem from discrete geometry, known as the complexity of k-levels in line arrangements. The main downside of divisor methods is their violation of the quota axiom, i.e., every state should receive ⌊qi⌋ or ⌈qi⌉ seats, where qi is the proportional share of the state. As we show that randomizing over divisor methods can only partially overcome this issue, we propose a relaxed version of divisor methods in which the total number of seats may slightly deviate from the house size. By randomizing over these methods, we can simultaneously satisfy population monotonicity, quota, and ex-ante proportionality. Finally, we turn our attention to quota-compliant methods that are house-monotone, i.e., no state may lose a seat when the house size is increased. We provide a polyhedral characterization based on network flows, which implies a simple description of all ex-ante proportional randomized methods that are house-monotone and quota-compliant. Copyright © 2024, The Authors. All rights reserved.

关键词： Combinatorial optimization

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Fast multiplication of random dense matrices with sparse matrices

Fast multiplication of random dense matrices with sparse mat...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Tianyu Liang Riley Murray Aydın Buluç James Demmel Electrical Engineering and Computer Science Department UC Berkeley Computational Research Division Lawrence Berkeley National Lab International Computer Science Institute Sandia National Laboratories

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

This work focuses on accelerating the multiplication of a dense random matrix with a (fixed) sparse matrix, which is frequently used in sketching algorithms. We develop a novel scheme that takes advantage of blocking and recomputation (on-the-fly random number generation) to accelerate this operation. The techniques we propose decrease memory movement, thereby increasing the algorithm’s parallel scalability in shared memory architectures. On the Intel Frontera architecture, our algorithm can achieve 2x speedups over libraries such as Eigen and Intel MKL on some examples. In addition, with 32 threads, we can obtain a parallel efficiency of up to 45%.We also present a theoretical analysis for the memory movement lower bound of our algorithm, showing that under mild assumptions, it’s possible to beat the data movement lower bound of general matrix-matrix multiply (GEMM) by a factor of M, where M is the cache size. Finally, we incorporate our sketching method into a randomized algorithm for overdetermined least squares with sparse data matrices. Our results are competitive with SuiteSparse for highly overdetermined problems; in some cases, we obtain a speedup of 10x over SuiteSparse.

关键词： Distributed processing Scalability Instruction sets Memory management Memory architecture Libraries Sparse matrices

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Leveraging Instruction-based Learning for Effective UAV Traffic Management in Dynamic Environment

Leveraging Instruction-based Learning for Effective UAV Traf...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： R.S. Ramya Keren Lois Daniel S. Rajkumar Sruthi S Madhavan Akshya. J Mani Deepak Choudhry Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore Department of Computer Science and Business Systems KGiSL Institute of Technology Coimbatore Department of Information Technology Sona College of Technology Salem Department of Artificial Intelligence and Data Science Nehru Institute of Engineering and Technology Coimbatore Department of Computational Intelligence SRM Institute of Science and Technology Chennai Department of Computing Technologies SRM Institute of Science and Technology Chennai

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

With the popularization of UAVs (Unmanned Aerial Vehicles) in surveillance, delivery services, environmental monitoring, and increasingly other operational aspects, besides the need to take off and land, efficient traffic management becomes integral for assuring and securing further development and exploitation. Most of the existing traffic management systems for UAVs depend on traditional rule-based algorithms or simpler machine learning techniques, such as DQN or PPO. However, these systems also have enormous challenges: dynamic environments, high-density traffic, and robust decision-making over unpredictable weather and network conditions. In addition, the above traditional models usually experience difficulties in balancing exploration and exploitation, leading to suboptimal routing and enlarged collision risks in complicated urban airspaces. These challenges have driven us to develop a new proposal: a UA V traffic management framework based on Double Deep Q-Network (DDQN) combined with Instruction-Based Learning. In this approach, advanced learning for DDQN optimizes processes at decision-making points for UAVs. The model proposed herein, along with comprehensive simulations and quantitative analyses, demonstrated higher performance in terms of leadership and cumulative rewards while keeping lower training loss compared to the classical algorithms such as DQN, A3C, and PPO. At the same time, the DDQN model presented a better success rate (95%), as also reflected in the balanced precision-recall curve showing an area under the curve of 0.90, over others in the on-point prediction of UAVstate and safety margin of collision. Our experimental results demonstrate that the proposed DDQN-based framework significantly enhances UA V traffic management by optimizing energy consumption in complex airspaces and reducing the risk of collision.

关键词： Training Analytical models Adaptation models Statistical analysis Atmospheric modeling computational modeling Decision making Predictive models Autonomous aerial vehicles Safety

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