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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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Automated Testing for Service-Oriented Architecture: Leveraging Large Language Models for Enhanced Service Composition

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IEEE Access 2025年 13卷 89627-89640页

作者： Altin, Mahsun Mutlu, Behcet Kilinc, Deniz Cakir, Altan Istanbul Technical University Department of Computer Engineering Istanbul34467 Turkey Wingie Enuygun Group Istanbul34750 Turkey University of Bakirçay Department of Computer Engineering Izmir35665 Turkey Istanbul Technical University Artificial Intelligence Data Science Research and Application Center Istanbul34467 Turkey ITU Ari Teknokent Parton Big Data Analytics and Consulting Istanbul34467 Turkey Adin.Ai WilmingtonDE19806 United States

This article explores the application of Large Language Models (LLMs), including proprietary models such as Openai's ChatGPT 4o and ChatGPT 4o-mini, Anthropic's Claude 3.5 Sonnet and Claude 3.7 Sonnet, and Google's Gemini 1.5 Pro, Gemini 2.0 Flash, and Gemini 2.0 Flash-Lite, as well as open-source alternatives including Qwen2.5-14B-Instruct-1M, and commercially accessed models such as DeepSeek R1 and DeepSeek V3, which were tested via APIs despite having open-source variants, to automate validation and verification in Application Programming Interface (API) testing within a Service-Oriented Architecture (SOA). Our system compares internal responses from the Enuygun Web Server against third-party API outputs in both JSON and Xml formats, validating critical parameters such as flight prices, baggage allowances, and seat availability. We generated 100 diverse test scenarios across varying complexities (1-4 flight results) by randomly altering request and response parameters. Experimental results show that Google Gemini 2.0 Flash achieved high accuracy (up to 99.98%) with the lowest completion time (85.34 seconds), while Qwen2.5-14B-Instruct-1M exhibited limited capability in processing complex formats. Models such as Openai's ChatGPT and Anthropic's Claude Sonnet models also demonstrated strong performance in single-flight validation scenarios, making them suitable for low-latency, high-precision tasks. Our findings indicate that some open-source models can offer promising cost-effective alternatives, though performance significantly varies. This integration of LLMs reduced manual workload, improved test scalability, and enabled real-time validation across large-scale datasets. As LLM technologies mature, we anticipate further advances in automation, accuracy, and efficiency in software validation systems. © 2013 IEEE.

关键词： Service oriented architecture (SOA)

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Smart Mood Detection: Real-Time Emotion Analysis for Human-computer Interaction Using Deep Learning

Smart Mood Detection: Real-Time Emotion Analysis for Human-C...

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Artificial Intelligence and Data engineering (aiDE), International Conference on

作者： Chandrappa S Tapabrata Banerjee Sohan Saha Mohd Zaid Aditya Pai H Guru Prasad M S Rajesh I S Dept. of Computer Science and Engineering JAIN (Deemed to-be University) Bengaluru India Dept. of Computer Science and Engineering(AI and DE) JAIN (Deemed to-be University) Bengaluru India Department of CSE MIT Art Design and Technology University Pune India Department of Computer Science and Engineering Graphic Era (Deemed to be University) Dehradun India Dept. of AI & ML BMS Institute of Technology and Management Bengaluru India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

A convolutional neural network (CNN)-based face emotion recognition system that analyzes human emotions in real-time, including happy, sorrow, rage, and surprise, is described. Existing approaches often struggle with accurately recognizing subtle or complex emotions due to limited feature extraction capabilities and the need for large, diverse training datasets. Additionally, they face challenges in adapting to real-world conditions, such as variations in lighting, angles, and occlusions. In order to improve the model's generalizability, the proposed research used a preprocessing pipeline that included gray scale conversion, scaling, normalization, and augmentation techniques including rotation and flipping on a labeled and balanced dataset of facial photos. Dropout and regularization techniques were used to reduce over fitting, and the CNN architecture was meticulously adjusted to maximize speed. High accuracy and resilience across emotion categories are demonstrated by preliminary results, indicating the model's potential for real-world uses in security surveillance, healthcare, and human-computer interaction.

关键词： Human computer interaction Training Emotion recognition Mood Face recognition Surveillance Real-time systems Convolutional neural networks Security Resilience

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