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

作者： Nair, Raveena R Sreevidya, D. Mohan, Chinnem Rama Banerjee, Joydeep Chouhan, Kuldeep Dharamvir Saveetha School of Law Chennai India and Ai & Ds Hyderabad India Narayana Engineering College Nellore Department of Computer Science and Engineering Andhra Pradesh India Nshm Knowledge Campus Durgapur West Bengal Kolkata India Sharda School of Engineering and Technology Department of Computer Science Applications Uttar Pradesh Greater Noida India The Oxford College of Engineering Department of Mca Bommanahalli Karnataka Bengaluru India

ISBN: (纸本)9798350350067

With an emphasis on multi-cloud networks, this research paper offers a synopsis of cloud computing techniques. It covers the fundamentals of cloud programming, the difficulties that cloud computing faces, and the ways that multi-cloud systems, such as cloud hybridization, federation clouds, cross-clouds, and multi-clouds, might help solve these problems. The opportunity of multi-cloud structures in combination with modern technologies such as Big Data and also Machine Learning (ml) is explored as well in the study, leading to some fascinating breakthroughs. To assist researchers, grasp the difficulties and generate enthusiasm within the community for the upcoming pairing of multi-cloud services with additional cutting-edge technologies, the aim is to present a contemporary perspective on multi-cloud technology and solve outstanding concerns. © 2024 IEEE.

关键词： Cloud platforms

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A Call for Clarity in Beam Search: How It Works and When It Stops 30

A Call for Clarity in Beam Search: How It Works and When It ...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Kasai, Jungo Sakaguchi, Keisuke Le Bras, Ronan Radev, Dragomir Choi, Yejin Smith, Noah A. Toyota Technological Institute Chicago United States Tohoku University Japan RIKEN Japan Allen Institute for AI Department of Computer Science Yale University United States Paul G. Allen School of Computer Science & Engineering University of Washington United States

ISBN: (纸本)9782493814104

Text generation with beam search has proven successful in a wide range of applications. We point out that, though largely overlooked in the literature, the commonly-used implementation of beam decoding (e.g., Hugging Face Transformers and fairseq) uses a first come, first served heuristic: it keeps a set of already completed sequences over time steps and stops when the size of this set reaches the beam size. Based on this finding, we introduce a patience factor, a simple modification to this beam decoding implementation, that generalizes the stopping criterion and provides flexibility to the depth of search. Empirical results demonstrate that adjusting this patience factor improves decoding performance of strong pretrained models on news text summarization and machine translation over diverse language pairs, with a negligible inference slowdown. Our approach only modifies one line of code and can be thus readily incorporated in any implementation. Further, we find that different versions of beam decoding result in large performance differences in summarization, demonstrating the need for clarity in specifying the beam search implementation in research work. Our code will be available upon publication. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Machine translation

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Dimensionality Reduction Method Based Feature Ranking Techniques for Diagnosis of Cardiac Disease Using ai 5

Dimensionality Reduction Method Based Feature Ranking Techni...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Ammisetty, Veeraswamy Pechetti, UmaSankar Rojarani, Kandula Bunyan, K John Chapala, Hari Kishan Shakunthala, B.S. Koneru Lakshmaiah Educational Foundation Department of Computer Science & Engineering Andhra Pradesh Guntur522302 India Department of CSE A.P Tadepalligudem India Aditya College of Engineering Department of AIML & IOT Andhra Pradesh East Godavari District India Bapatla Engineering College Department CB&DS Andhra Pradesh Bapatla India St.Ann's College of Engineering & Technology Department of AI & ML Andhra Pradesh Chirala India Kalpataru Institute of Technology Department of ISE Karnataka Tumkur Dist Tiptur572202 India

ISBN: (纸本)9798331530013

The computational time and cost of medical diseases diagnosis are so critical. One possible solution to reduce the dataset size of the medical data, ideally without losing predictive performance. High-dimensional data poses a severe challenge for data mining. This paper presents a systematic survey of the computational steps in automated disease diagnosis based on its features (attributes).These computational steps are: 1.) feature extraction to quantify the important features of the disease 2.) Ranking algorithms to reduce the dimensions (features) obtained after feature extraction 3.)Obtaining the final reduced number of features using feature Re-ranking algorithms and 4.) Classifying the selected features to identify the type of disease. Among various dimensionality reduction methods, PCA plays a vital role. However, the experiments also show that PCA is more sensitive to the choice of reduced dimensions. After reaching a peak, the various classifier accuracy increases performing feature selection using Ranking algorithms and Re-Ranking algorithms of dimensions for PCA. © 2024 IEEE.

关键词： Dimensionality reduction

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Biosensor in Climate Change and Water Rise Analysis Based on Diverse Biological Ecosystems Using Machine Learning Model

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 500-511页

作者： Kalpana, V. Durairaj, M. Poonguzhali, S. Tham, Jacquline Srinivas, B. Biban, Laxmi Priyadarsan, Padhyala Vel Tech Rangarajan Dr. Sagunthala R & amp D Institute of Science and Technology Chennai 600062 India Department of ECE Saveetha School of Engineering SIMATS Chennai India Roever Agriculture College Tamil Nadu Perambalur India Management and Science University Selangor Shah Alam Malaysia Department of AI & amp ML Aditya University AP Surampalem India Department of Botany Pt. C.L.S. Govt. College Haryana Karnal India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Dist Andhra Pradesh Vaddeswaram 522302 India

The stability of environmental conditions around the world has been threatened by climate change in the last few decades. Sea levels are rising more quickly than ever before due to a combination of factors including temperature increases, changes in precipitation patterns, and glacier melting. We describe a machine learning strategy to design coastal sea level fluctuation as well as related uncertainty across a range of timescales by utilising important ocean temperature estimations as proxies for regional thermosteric sea level component. The aim of this research is to propose novel method in climate change with water level rise analysis using biosensor with machine learning techniques in diverse biological ecosystems. Here, the input is collected as biosensor-based climate analysis with water level rise analysis dataset and processed for noise removal, normalisation, and smoothening. Then, climate data analysis is carried out utilising fuzzy adversarial encoder (FAE) model and the water level rise analysis is carried out using recurrent transfer AlexNet neural network (RTAlexNetNN). The classified output shows climate change analysis with water level rise modelling. The experimental analysis has been carried out for various climate data and water rise data in terms of random accuracy, specificity, MSE, F-measure, and normalised cross-correlation. The proposed technique obtained 98% random accuracy, 96% normalised cross-correlation, 94% specificity, 58% MSE, and 92% F-measure. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Biosensor Climate change Diverse biological ecosystems Machine learning techniques Water level rise analysis

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LayerMerge: Neural Network Depth Compression through Layer Pruning and Merging 41

LayerMerge: Neural Network Depth Compression through Layer P...

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41st International Conference on Machine Learning, ICml 2024

作者： Kim, Jinuk El Halabi, Marwa Ji, Mingi Song, Hyun Oh Department of Computer Science and Engineering Seoul National University Korea Republic of Neural Processing Research Center Korea Republic of Samsung - SAIT AI Lab Montreal Canada Google United States

Recent works show that reducing the number of layers in a convolutional neural network can enhance efficiency while maintaining the performance of the network. Existing depth compression methods remove redundant non-linear activation functions and merge the consecutive convolution layers into a single layer. However, these methods suffer from a critical drawback;the kernel size of the merged layers becomes larger, significantly undermining the latency reduction gained from reducing the depth of the network. We show that this problem can be addressed by jointly pruning convolution layers and activation functions. To this end, we propose LayerMerge, a novel depth compression method that selects which activation layers and convolution layers to remove, to achieve a desired inference speedup while minimizing performance loss. Since the corresponding selection problem involves an exponential search space, we formulate a novel surrogate optimization problem and efficiently solve it via dynamic programming. Empirical results demonstrate that our method consistently outperforms existing depth compression and layer pruning methods on various network architectures, both on image classification and generation tasks. We release the code at https://***/snu-mllab/LayerMerge. Copyright 2024 by the author(s)

关键词： Dynamic programming

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mlPDR: High Performance ml Algorithms for the Prediction of Diabetes Retinopathy

MLPDR: High Performance ML Algorithms for the Prediction of ...

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2023 IEEE International Conference on Network, Multimedia and Information Technology, NMITCON 2023

作者： Madhuri G, Sindhu Chokkanathan, K. Mahesh, T.R. Musthafa, M.Mohamed Vanitha, K. Vivek, V. Manipal Institute of Technology Department of Information Technology Bengaluru India Manipal Academy of Higher Education Manipal India Madanapalle Institute of Technology & Science Department of Ai Madanapalle India Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India Tamil Nadu Erode India Karpagam Academy of Higher Education Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350300826

In today's world, millions of imaging analyses are performed every week, and medical imaging plays a significant role in that. It is a process of creating visual representation based on the functioning of human organs or tissues to identify abnormalities or study diseases. Diabetes has been regarded as one of the most prevalent diseases by many researchers. Blindness or other retinal alterations based on the eye's vision, such as difficulty reading or seeing distant things, may occur, which has an impact on diabetes in humans. A new computer aided diagnosis system based on image processing of retinal images can be analyzed in order to detect diabetic patients in advance. In this study, the eye retina based on image processing techniques is analyzed and machine learning techniques have been used to extract features of eye and several computational intelligence Machine Learning techniques like Support Vector Machines (SVM) and Principal Component Analysis (PCA). These Computational Intelligence techniques have been used to detect the diabetic in advance. Several performance metrics have been considered such as Root Mean Square Error (RMSE), Peak Signal Noise Ratio (PSNR), Entropy (E), and Accuracy (A) in obtaining the better accuracy results for detection of diabetic retinopathy in early stages. The experimental results show that, SVM outperforms with an accuracy of 95%. © 2023 IEEE.

关键词： Principal component analysis

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Terahertz Communications and Sensing Overview 3

Terahertz Communications and Sensing Overview

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3rd International Conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Salama, Ramiz Altrjman, Chadi Al-Turjman, Fadi Yadav, Satya Prakash Vats, Satvik Gupta, Richa Ai and Robotics Institute Research Center for Ai and IoT Near East University Department of Computer Engineering Near East Boulevard TRNC Mersin 10 Nicosia99138 Turkey Waterloo University Department of Chemical Engineering ONN2L 3G1 Canada Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept Mersin10 Nicosia Turkey Department of Computer Science and Engineering Greater Noida201306 India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Raj Kumar Goel Institute of Technology Department of Electronics & Communication Engineering India

ISBN: (纸本)9798350330724

Terahertz (THz) (0.1-10 THz) wireless communication is one of the cornerstones of the next 6G wireless networks. THz frequencies have the ability to dramatically increase wireless capacity performance and enable high-resolution environment sensing if applied properly due to the enormous quantity of accessible bandwidth. However, the usage of wireless devices in high-frequency bands like THz is constrained by a very unpredictable and dynamic channel. The ultimate result is essentially unreliable intermittent connections since these channel constraints have a naturally restricted communication range and a high susceptibility to blocking and chemical absorption. Because of this, the THz band's potential for high-rate communications and high-resolution sensing may be hindered. This study thoroughly examines the steps necessary to build up and operate next-generation THz wireless networks that will work together to deliver a variety of communication and sensing services in this environment. We first lay the groundwork for this by defining the THz frequency range's fundamentals. Using these fundamentals as a foundation, we outline and carefully investigate seven specific qualities that characterize THz wireless systems: Some of the subjects discussed include the quasi-opticality of the band, wireless architectures suited for THz, synergy with lower frequency bands, cooperative sensing and communication systems, PHY-layer protocols, spectrum access techniques, and real-time network optimization. These seven distinctive features enable our understanding of how to re-engineer wireless systems as we know them today to fit THz bands and their specific settings. On the one hand, THz systems make use of its quasi-optimality and may turn any sensing opportunity into a communication problem, aiding in the development of a new breed of flexible wireless systems that can do many jobs beyond straightforward communications. THz systems can alternatively use intelligent surfaces, lower

关键词： Internet of Everything

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Smart Operating Range Monitoring of Solar PV Cell with Integrated Phase Change Materials by Using Solar Deep Learning Model

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Electric Power Components and Systems 2024年第11期52卷 2147-2158页

作者： Geetha, P. Ajitha, S. Jyothirmayi, M. Guha, Tapas Chaturvedi, Abhay Ganeshan, P. Taqui, Syed Noeman Al-Ammar, Essam A. Wabaidur, Saikh Mohammad Iqbal, Amjad Department of Electronics and Communication Engineering Sree Vidyanikethan Engineering College Andhra Pradesh Tirupati India Department of MCA Ramaiah Institute of Technology Karnataka Bengaluru India Department of Electronics and Instrumentation Engineering Ramaiah Institute of Technology Karnataka Bengaluru India Department of AI and ML Nitte Meenakshi Institute of Technology Karnataka Bengaluru India Department of Electronics & Communication Engineering GLA University Uttar Pradesh Mathura India Department of Mechanical Engineering Sri Eshwar Engineering College Tamilnadu Coimbatore India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India Department of Electrical Engineering College of Engineering King Saud University Riyadh Saudi Arabia Chemistry Department College of Science King Saud University Riyadh Saudi Arabia Department of Materials Technologies Faculty of Materials Engineering Silesian University of Technology Gliwice Poland

This research aims to demonstrate that a one-of-a-kind microencapsulated bio-based PCM may be utilized in the passive collection of solar energy in buildings. The research makes use of a PCM with thermal properties within the comfort zone but is also able to track the performance of solar photovoltaic panels. This allows the research to be more accurate. Because of this, the research can produce outcomes that are more accurate. After finishing the step before this one, the next thing that needs to be done is creating novel composite concrete panels. This step follows the completion of the phase before it. The outcomes of the mechanical and thermal tests served as the basis for determining the best composition of the concrete mix that should be used for making this panel. The method concludes with the construction of a test structure using composite walls as the primary building material. © 2023 Taylor & Francis Group, LLC.

关键词： Solar energy

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Blockchain Technology and Artificial Intelligence's Future Applications in Cyber Security 3

Blockchain Technology and Artificial Intelligence's Future A...

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3rd International Conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Salama, Ramiz Al-Turjman, Sinem Altrjman, Chadi Al-Turjman, Fadi Gupta, Richa Yadav, Satya Prakash Vats, Satvik Ai and Robotics Institute Research Center for Ai and IoT Near East University Department of Computer Engineering Near East Boulevard TRNC Mersin 10 Nicosia99138 Turkey Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept Mersin10 Nicosia Turkey Waterloo University Department of Chemical Engineering ONN2L 3G1 Canada Raj Kumar Goel Institute of Technology Department of Electronics & Communication Engineering India Department of Computer Science and Engineering Greater Noida201306 India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350330724

Artificial intelligence and blockchain are quickly integrating in daily life and business applications. When numerous information systems must access and analyze data in real-time in centralized systems and applications, such as healthcare, a bottleneck develops. This issue would be resolved by blockchain's decentralized database architecture, safe data storage, data exchange, and authentication. Additionally, ai can be present at the very top of the blockchain and produce insights from the shared data that is created and applied to forecasts. Blockchain is a cutting-edge Cybersecurity technology that creates chains by mutual consent between nodes that chronologically link new blocks to those previously stored in nodes. A number of industries, including banking, insurance, cybersecurity, forecasting, healthcare, and cryptocurrencies, among others, are growing more quickly as a result of technology convergence. The likelihood of these systems being hacked increases as more digital technologies are used and these businesses provide services. Blockchain technology and artificial intelligence can work together to create a strong defense against these dangers and security problems. We'll examine how blockchain and ai are merged in cybersecurity in this chapter. We'll go into more detail about how they help secure cyber-physical systems. © 2023 IEEE.

关键词： Blockchain

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Aligning diffusion behaviors with Q-functions for efficient continuous control 24

Aligning diffusion behaviors with Q-functions for efficient ...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Huayu Chen Kaiwen Zheng Hang Su Jun Zhu Department of Computer Science and Technology Tsinghua University and Institute for AI BNRist Center Tsinghua-Bosch Joint ML Center THBI Lab Tsinghua University Department of Computer Science and Technology Tsinghua University and Institute for AI BNRist Center Tsinghua-Bosch Joint ML Center THBI Lab Tsinghua University and Pazhou Lab (Huangpu) Guangzhou China

ISBN: (纸本)9798331314385

Drawing upon recent advances in language model alignment, we formulate offline Reinforcement Learning as a two-stage optimization problem: First pretraining expressive generative policies on reward-free behavior datasets, then fine-tuning these policies to align with task-specific annotations like Q-values. This strategy allows us to leverage abundant and diverse behavior data to enhance generalization and enable rapid adaptation to downstream tasks using minimal annotations. In particular, we introduce Efficient Diffusion Alignment (EDA) for solving continuous control problems. EDA utilizes diffusion models for behavior modeling. However, unlike previous approaches, we represent diffusion policies as the derivative of a scalar neural network with respect to action inputs. This representation is critical because it enables direct density calculation for diffusion models, making them compatible with existing LLM alignment theories. During policy fine-tuning, we extend preference-based alignment methods like Direct Preference Optimization (DPO) to align diffusion behaviors with continuous Q-functions. Our evaluation on the D4RL benchmark shows that EDA exceeds all baseline methods in overall performance. Notably, EDA maintains about 95% of performance and still outperforms several baselines given only 1% of Q-labelled data during fine-tuning. Code: https://***/thu-ml/Efficient-Diffusion-Alignment

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