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2nd International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2023

作者： Vincent, Robin Rohit Sarkar, Swagata Irence, Shalom Prakash, R.B.R. Senthil Rama, R. Suganthi, D. School of Computer Science & Engineering and Information Science Computer Science & Engineering Presidency University Bengaluru560064 India Artificial Intelligence and Data Science Sri Sairam Engineering College Chennai 44 India EEE Karunya Kits / CBE India Koneru Lakshmaiah Education Foundation Electrical and Electronics Engineering Vaddeswaram522302 India DMI College of Engineering EEE Chennai India Saveetha College of Liberal Arts and Sciences Department of Computer Science Saveetha Institute of Medical and Technical Sciences Chennai India

ISBN: (纸本)9798350397635

Due to fast expansion and improved lifestyle, the need for useable energy has skyrocketed in recent decades, notably in the construction industry. Several factors, such as local climate, building makeup, and energy consumption habits, affect a structure's efficiency in using energy. In this paper, we aim for forecasting the interior temperature of smart buildings by combining IoT with popular machine Learning algorithms. This prediction model has been developed using online learning techniques to increase its adaptability to novel inputs. This paper details a methodology for creating an analytical model that may be utilized for smart building interior temperature forecasting by merging the IoT with some popular machine learning methods. This prediction model was built with an online-learning strategy, so it may be used for a dataset with which the developers are unfamiliar. To ensure the precision of the method, this article uses Machine Learning to test it using real-world sensor data. In order to verify the methodology, the study conducts experiments using Machine Learning on collected, real-world sensor data. The study then proposes incorporating the following method into an Edge computing based IoT architecture in order to make the building operational in an energy-efficient manner. The learning framework was validated with data collected from an Internet of Things network installed in a home over the course of a summertime. The learnt model's efficacy has been evaluated, and the model's predictability of interior temperature changes has been quantified. The paper demonstrates the viability of the proposed Learning framework for thermal models based on the Internet of Things and provides a pragmatic answer to the problem of supplying accurate thermal models for use in the temperature controls of smart buildings of the future. © 2023 IEEE.

关键词： Energy utilization

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The Role of Edge computing in Enhancing Cloud Performance and Reducing Latency 7

The Role of Edge Computing in Enhancing Cloud Performance an...

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7th International Conference on Contemporary computing and Informatics, IC3I 2024

作者： Sharma, Puneet Shyamala Devi, J. Manikandan, K. Ani Minisha, R. Harini, R. Karthik, S. Sr University School of Cs & Ai Telangana Warangal India Rajalakshmi Institute of Technology Chennai India Tamilnadu Vellore632014 India R.M. K. Engineering College Department of Artificial Intelligence and Data Science India Veltech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Computer Science and Engineering India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Mathematics Avadi Tamilnadu Chennai600062 India

ISBN: (纸本)9798350350067

The flexibility of implementing edge computing over cloud infrastructures has become essential for responding to the need for reducing latency and improving data processing in a distributed environment. This research aims to determine the use of edge computing in enhancing the effectiveness of cloud utilization through conducting machine learning investigations utilizing Random Forest in the determination of the optimum contribution of resources. The work also set up and applied real-world datasets from Kaggle to illustrate that edge computing can alleviate the latency issue mainly by offloading computationally intensive tasks to edge nodes away from the central cloud. The results show that edge computing with the help of Machine learning is the best solution for cutting-edge cloud environments as it is fast and does not aggravate the problems of excessive use of resources. Therefore, this research will be a great addition to the existing literature on the integration of cloud and edge computing and is hoped to offer useful information for future developments in the field of cloud computing. © 2024 IEEE.

关键词： Cloud platforms

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Multi-View Incremental Learning with Structured Hebbian Plasticity for Enhanced Fusion Efficiency

arXiv

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arXiv 2024年

作者： Chen, Yuhong Song, Ailin Yin, Huifeng Zhong, Shuai Chen, Fuhai Xu, Qi Wang, Shiping Xu, Mingkun College of Computer and Data Science Fuzhou University Fuzhou China Guangdong Institute of Intelligence Science and Technology Hengqin Zhuhai China University of Electronic Science and Technology of China Chengdu China Center for Brain Inspired Computing Research Department of Precision Instrument Tsinghua University Beijing China School of Computer Science and Technology Dalian University of Technology Dalian China

The rapid evolution of multimedia technology has revolutionized human perception, paving the way for multi-view learning. However, traditional multi-view learning approaches are tailored for scenarios with fixed data views, falling short of emulating the intricate cognitive procedures of the human brain processing signals sequentially. Our cerebral architecture seamlessly integrates sequential data through intricate feed-forward and feedback mechanisms. In stark contrast, traditional methods struggle to generalize effectively when confronted with data spanning diverse domains, highlighting the need for innovative strategies that can mimic the brain’s adaptability and dynamic integration capabilities. In this paper, we propose a bio-neurologically inspired multiview incremental framework named MVIL aimed at emulating the brain’s fine-grained fusion of sequentially arriving views. MVIL lies two fundamental modules: structured Hebbian plasticity and synaptic partition learning. The structured Hebbian plasticity reshapes the structure of weights to express the high correlation between view representations, facilitating a fine-grained fusion of view representations. Moreover, synaptic partition learning is efficient in alleviating drastic changes in weights and also retaining old knowledge by inhibiting partial synapses. These modules bionically play a central role in reinforcing crucial associations between newly acquired information and existing knowledge repositories, thereby enhancing the network’s capacity for generalization. Experimental results on six benchmark datasets show MVIL’s effectiveness over state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Adaptive Latent Factor Analysis via Generalized Momentum-Incorporated Particle Swarm Optimization 19

Adaptive Latent Factor Analysis via Generalized Momentum-Inc...

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19th IEEE International Conference on Networking, Sensing and Control, ICNSC 2022

作者： Chen, Jiufang Yuan, Ye School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing400714 China Chongqing School University of Chinese Academy of Sciences Chongqing400714 China College of Computer and Information Science Southwest University Chongqing400715 China

ISBN: (纸本)9781665472432

Stochastic gradient descent (SGD) algorithm is an effective learning strategy to build a latent factor analysis (LFA) model on a high-dimensional and incomplete (HDI) matrix. A particle swarm optimization (PSO) algorithm is commonly adopted to make an SGD-based LFA model's hyper-parameters, i.e., learning rate and regularization coefficient, self-adaptation. However, a standard PSO algorithm may suffer from accuracy loss caused by premature convergence. To address this issue, this paper incorporates more historical information into each particle's evolutionary process for avoiding premature convergence following the principle of a generalized-momentum (GM) method, thereby innovatively achieving a novel GM-incorporated PSO (GM-PSO). With it, a GM-PSO-based LFA (GMPL) model is further achieved to implement efficient self-adaptation of hyper-parameters. The experimental results on three HDI matrices demonstrate that the GMPL model achieves a higher prediction accuracy for missing data estimation in industrial applications. © 2022 IEEE.

关键词： Momentum

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Detection and Segmentation of Anomalous Traffic Signs Using Yolov8 and Segment Anything Model (SAM) for Indian Specific Conditions

Detection and Segmentation of Anomalous Traffic Signs Using ...

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IEEE International Conference on Electronics, computing and Communication Technologies (CONECCT)

作者： Divya Varshney Tirtharaj Pal Indrajit Ghosh Department of Civil Engineering Indian Institute of Technology Roorkee Roorkee India Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Roorkee Roorkee India

ISBN: (数字)9798350385922

ISBN: (纸本)9798350385939

Road signs are critical elements of road safety, but anomalies in these signs can lead to confusion and accidents. This paper presents a multi-stage approach to detecting and rectifying anomalous road signs in the Indian context. Leveraging deep learning algorithms like YOLOv8 and innovative methodologies such as synthetic data generation and autoencoder-based anomaly detection, we aim to enhance road sign management systems' accuracy and efficacy. We collected data along various routes, trained YOLOv8 with both normal and synthetic anomalous signs, and employed SAM for segmentation. Our results demonstrate promising performance metrics, indicating the effectiveness of our approach. Future work involves expanding the scope to include more anomalies and intensive autoencoder training, ultimately contributing to global road safety and the future of autonomous vehicles.

关键词： Training Measurement Deep learning Computational modeling Road safety data models Communications technology

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The Necessary Requirements for Utilizing AI in data Access Setting 15

The Necessary Requirements for Utilizing AI in Data Access S...

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

作者： Swapnil, K. Sharma, Bhuvnesh Singh, Manpreet Yadav, Rakesh Kumar Devaraj Verma, C. Umarani, K. Vishwakarma Institute of Technology Department of Artificial Intelligence &data Science Pune India Vivekananda Global University Department of Mechanical Engineering Jaipur India Maharishi University of Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Ramnagar District562112 India Chitkara University Institute of Engineering and technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350370249

AI has been an increasing number of adopted in records get admission to settings to improve the overall performance of statistics get admission to and extract significant insights, using extended productiveness and value-effectiveness. However, the success implementation of AI calls for positive essential requirements to be met, together with facts accuracy, connectivity, security, and scalability. First, facts accuracy is critical for AI applications in facts get entry to settings. Information gathered need to be of excessive quality and accuracy, as AI working on erroneous facts can yield unreliable and probably harmful effects. Records should be of suitable granularity, taking pictures the most relevant elements, to make certain a complete and steady training performance.2nd, linked statistics assets, networks, and services are had to leverage the capability of AI in information get entry to settings. This gives an interconnected and up to date dataset at scale, while requiring included answers for uninterrupted get entry to. Moreover, single sign-on and alertness orchestration abilities are vital to decorate user revel in. © 2024 IEEE.

关键词： Connectivity Gathered Increasing Productiveness Security

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MassSpecGym: A benchmark for the discovery and identification of molecules 38

MassSpecGym: A benchmark for the discovery and identificatio...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Bushuiev, Roman Bushuiev, Anton de Jonge, Niek F. Young, Adamo Kretschmer, Fleming Samusevich, Raman Heirman, Janne Wang, Fei Zhang, Luke Dührkop, Kai Ludwig, Marcus Haupt, Nils A. Kalia, Apurva Brungs, Corinna Schmid, Robin Greiner, Russell Wang, Bo Wishart, David S. Liu, Li-Ping Rousu, Juho Bittremieux, Wout Rost, Hannes Mak, Tytus D. Hassoun, Soha Huber, Florian van der Hooft, Justin J.J. Stravs, Michael A. Böcker, Sebastian Sivic, Josef Pluskal, Tomáš Institute of Organic Chemistry and Biochemistry The Czech Academy of Sciences Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Czech Republic Bioinformatics Group Wageningen University & Research Netherlands Department of Computer Science University of Toronto Canada Bioinformatics Institute for Computer Science Friedrich Schiller University Jena Germany Department of Computer Science University of Antwerp Belgium Department of computing science University of Alberta Canada Alberta Machine Intelligence Institute Canada Department of Molecular Genetics University of Toronto Canada Bright Giant GmbH Department of Computer Science Tufts University United States Department of Biological Sciences University of Alberta Canada Department of Computer Science Aalto University Finland Mass Spectrometry Data Center National Institute of Standards and Technology United States Department of Chemical and Biological Engineering Tufts University United States Centre for Digitalisation and Digitality University of Applied Sciences Düsseldorf Germany Department of Biochemistry University of Johannesburg South Africa Eawag Swiss Federal Institute of Aquatic Science and Technology Switzerland

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym - the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: de novo molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at https://***/pluskal-lab/MassSpecGym. © 2024 Neural information processing systems foundation. All rights reserved.

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Enhancing CNN Weights for Improved Routing in UAV Networks for Catastrophe Relief with MSBO Algorithm

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Journal of Mobile Multimedia 2024年第5期20卷 1117-1152页

作者： Gupta, Sachin Kumar Ul Din, Suhail Mohi Upreti, Kamal Mahajan, Shubham Date, Saroj S. Department of Electronics and Communication Engineering Central University of Jammu Jammu Samba 181143 India Department of Electronics and Communication Engineering Indian Institute of Technology Chhattisgarh Bhilai 491002 India Department of Computer Science Christ University Delhi NCR 201003 India Amity School of Engineering and Technology (ASET) Amity University Gurugram Haryana Panchgaon 122412 India Department of Artificial Intelligence and Data Science CSMSS Chh Shahu College of Engineering Maharashtra Chh. Sambhajinagar India

UAVs have become key in various applications lately, from catastrophe relief to environmental monitoring. The plan of powerful and reliable directing protocols in UAV networks is seriously hampered by the dynamic and habitually eccentric mobility patterns of UAVs. This study proposes a novel technique to beat these challenges by utilizing the Modified Smell Bees Optimization (MSBO) algorithm to upgrade the weights of CNNs. This study’s principal objective is to further develop UAV network routing decisions by using CNNs’ ability for design recognition and the Modified SBO’s optimization abilities. Our methodology comprises of randomly relegating CNN weights to a populace of bees at start, evaluating their wellness by fitness of directing performance, and iteratively fine-tuning these weights utilizing local and global search procedures got from bee searching. Broad simulations and performance evaluations show that our recommended approach incredibly expands the general dependability of UAV’s, brings down communication latency, and improves directing productivity. Future exploration in UAV network improvement gives off an impression of being going in a promising direction with the integration of CNNs for pattern recognition and the Modified SBO for weight enhancement. In addition to progressing UAV routing conventions, this work sets out new open doors for machine learning applications of bio-inspired optimization algorithms. © 2024 River Publishers.

关键词： catastrophe relief CNN Modified smell bees optimization (MSBO) optimization algorithms routing protocols UAV networks

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On Statistical Rates and Provably Efficient Criteria of Latent Diffusion Transformers (DiTs)

arXiv

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arXiv 2024年

作者： Hu, Jerry Yao-Chieh Wu, Weimin Song, Zhao Liu, Han Center for Foundation Models Generative AI Department of Computer Science Department of Statistics and Data Science Northwestern University EvanstonIL60208 United States Simons Institute for the Theory of Computing UC Berkeley BerkeleyCA94720 United States

We investigate the statistical and computational limits of latent Diffusion Transformers (DiTs) under the low-dimensional linear latent space assumption. Statistically, we study the universal approximation and sample complexity of the DiTs score function, as well as the distribution recovery property of the initial data. Specifically, under mild data assumptions, we derive an approximation error bound for the score network of latent DiTs, which is sub-linear in the latent space dimension. Additionally, we derive the corresponding sample complexity bound and show that the data distribution generated from the estimated score function converges toward a proximate area of the original one. Computationally, we characterize the hardness of both forward inference and backward computation of latent DiTs, assuming the Strong Exponential Time Hypothesis (SETH). For forward inference, we identify efficient criteria for all possible latent DiTs inference algorithms and showcase our theory by pushing the efficiency toward almost-linear time inference. For backward computation, we leverage the low-rank structure within the gradient computation of DiTs training for possible algorithmic speedup. Specifically, we show that such speedup achieves almost-linear time latent DiTs training by casting the DiTs gradient as a series of chained low-rank approximations with bounded error. Under the low-dimensional assumption, we show that the statistical rates and the computational efficiency are all dominated by the dimension of the subspace, suggesting that latent DiTs have the potential to bypass the challenges associated with the high dimensionality of initial data. © 2024, CC BY-NC-SA.

关键词： Distribution transformers

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Optimized Hybrid CNN-GRU Approach for Accurate Prediction of Cardiovascular Diseases: Integrating Medical Imaging and Clinical data

Optimized Hybrid CNN-GRU Approach for Accurate Prediction of...

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Communication, Control and Intelligent Systems (CCIS)

作者： Prasanna V Suresh Kumar N Divyaprabha P R Sundaram Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Information Technology Sri Ramakrishna Engineering College Coimbatore India Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Computer Science and Engineering Ganesh College of Engineering Salem India

ISBN: (数字)9798331528201

ISBN: (纸本)9798331528218

As the primary cause of death globally, cardiovascular diseases (CVDs) demand precise and timely prediction to enhance patient outcomes. Other examples of conventional approaches for CVD prediction include statistical models as well as machine learning models such as a single model that often struggle to address complex and comprehensive data from clinical records and Medical Imaging. To address these issues, the concept proposed here is to integrate the medical imaging data with clinical data as new apart from utilizing the hybrid CNN–GRU model to predict cardiovascular diseases with higher accuracy and interpretability. While the GRU module deals with the time sequences of clinical data including past history and other test results, the CNN module is to extract the spatial features from the medical images. Thus, by integrating these modules, it is more accurate to predict the data of the patient as a synergy of spatial and temporal arrangements. Cross-validation techniques and performance indices are used for model training and testing on a diverse data set containing imaging and other clinical-related data. Experimental results depict that the suggested hybrid CNN-GRU model performs much better than the traditional approaches and has higher levels of predicted accuracy and robustness. It is also feasible to generalize this integrated strategy to other medical disorder where multi-modal data analysis is crucial, besides enhancing the capacity to estimate CVDs. The findings underline the importance of integrating doctors’ information with radiological images and applying new patterns of neural networks to augmenting clinical judgment. The suggested work is implemented using python.

关键词： Analytical models Accuracy data analysis Computational modeling Predictive models Feature extraction data models Convolutional neural networks Cardiovascular diseases Biomedical imaging

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