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2nd IEEE International Conference on IoT, Communication and Automation technology, ICICAT 2024

作者： Pandey, Supriya Pandey, Shrawan Kumar Tripathi, Abhinandan Buddha Institute of Technology Department of Computer Science & Engineering Gorakhpur India

ISBN: (纸本)9798350368109

The proliferation of online data, video, and audio transfer over the internet has made Reversible Data Hiding (RDH) a significant area of research. However, embedding data into an image can be challenging as it can cause distortion and degrade image quality. To mitigate this issue, we have proposed edge-preserving image smoothing before data embedding to reduce distortion. The outcomes of proposed schemes are evaluated using metrics such as Peak Signal to Noise Ratio (PSNR), encoding capacity (EC) including incorporation time and embedding speed. The study reveals that the proposed technique surpasses existing state-of-the-art methods. © 2024 IEEE.

关键词： EC PSNR RDH

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A Method to Reduce the Incidence of Wheat Leaf Diseases

A Method to Reduce the Incidence of Wheat Leaf Diseases

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2024 International Conference on Advances in Computing Research on science Engineering and technology, ACROSET 2024

作者： Verma, Riya Rathore, Narendra Pal Singh Sethi, Kamal Kumar Acropolis Insitute of Technology and Research Department of Computer Science Indore India

ISBN: (纸本)9798350388800

Numerous studies have delved into the domain of wheat disease prediction, frequently leveraging meticulously curated datasets compiled by researchers. This review comprehensively examines a spectrum of research endeavours, with a primary focus on elucidating classification methodologies and the breadth of disease manifestations analysed. By systematically surveying diverse studies, we scrutinize the approaches employed in interpreting disease patterns and the methodologies employed for data collection, while also addressing their inherent limitations. Through this thorough analysis, we shed light on the intricate facets of wheat disease prediction, providing invaluable insights into the current state of the field and delineating promising avenues for future research and development. © 2024 IEEE.

关键词： CNNs (Convolutional Neural Network) DI (Deep Learning) Leaf Diseases VGG Model

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Transfer Learning using IndoBERT with Long Short-Term Memory Classifier for Detection of Suicide Ideation Themed Indonesian Twitter Posts 4

Transfer Learning using IndoBERT with Long Short-Term Memory...

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4th International Conference of science and Information technology in Smart Administration, ICSINTESA 2024

作者： Setiawan, Michael Dany Kimberly, Sofie Suharjo, Felix Antonius Harefa, Jeklin Bina Nusantara University School of Computer Science Computer Science Department Alam Sutera Indonesia

ISBN: (纸本)9798350376111

Indonesia's lack of profiling on suicide and an overall negative mindset towards mental health issues contributes to a lack of effective suicide prevention methods, particularly in the scope of social media as a platform to express suicide ideations, with little to no studies having been made regarding building robust models aimed at suicide ideation detection of Indonesian language posts. This paper presents eight distinct text classification models fine-tuned to effectively detect suicide ideation in Indonesian language Twitter posts by combining four variants of pre-trained IndoBERT models with a convolutional neural network (CNN) or long short-term memory (LSTM) network through transfer learning. Each of these eight models are trained using a dataset of 17464 Twitter posts collected through querying Twitter using Indonesian phrases comprising suicide and non-suicide themed words, which is then annotated and preprocessed through lemmatization, emoji, user tag, and link removal. It is found that an IndoBERT large-p1 pre-trained model combined with a LSTM network using transfer learning offers the highest performance in the detection of suicide ideation in Indonesian language Twitter posts, boasting a 91.41% accuracy rate, 0.9570 recall score, 0.8830 precision score, 0.9185 F-score, and 0.9675 receiver operating characteristic (ROC) area under curve (AUC) score, achieving high overall performance and minimizing undetected suicidal posts. This is attributed to IndoBERT large's high number of parameters, coupled with LSTM's capability of storing long-spanning features within the dataset, allowing the model to effectively understand context within a post and differentiate suicidal and non-suicidal Twitter posts. © 2024 IEEE.

关键词： Tweets

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Fixed-time command filtered output feedback control for twin-roll inclined casting system with prescribed performance

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Mathematical Biosciences and Engineering 2024年第2期21卷 2282-2301页

作者： Gao, Dongxiang Zhang, Yujun Wu, Libing Liu, Sihan School of Computer and Software Engineering University of Science and Technology Liaoning Liaoning CO Anshan114051 China School of Science University of Science and Technology Liaoning Liaoning CO Anshan114051 China

The article investigates the issue of fixed-time control with adaptive output feedback for a twin-roll inclined casting system (TRICS) with disturbance. First, by using the mean value theorem, the nonaffine functions are decoupled to simplify the system. Second, radial basis function neural networks (RBFNNs) are introduced to approximate an unknown term, and a nonlinear neural state observer is created to handle the effects of unmeasured states. Then, the backstepping design framework is combined with prescribed performance and command filtering techniques to demonstrate that the scheme proposed in this article guarantees system performance within a fixed-time. The control design parameters determine the upper bound of settling time, regardless of the initial state of the system. Meanwhile, it ensures that all signals in the closed-loop system (CLS) remain bounded, and it can also maintain the tracking error within a predefined range within a fixed time. Finally, simulation results assert the effectiveness of the method. © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License.

关键词： Closed loop systems

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Sentimental Analysis-Based Recommended System for Products Using Machine Learning 8th

Sentimental Analysis-Based Recommended System for Products U...

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8th International Conference on Emerging Research in Computing, Information, Communication and Applications, ERCICA 2023

作者： Mamatha Bai, B.G. Likhith, S.R. Itagi, Salma Department of Computer Science Engineering Nitte Meenakshi Institute of Technology Karnataka Bengaluru India Department of School of Computer Science and Engineering and Information Science Bio-Intelligence Lab Presidency University Karnataka Bengaluru India Department of Computer Science Engineering Sai Vidya Institute of Technology Karnataka Bengaluru India

ISBN: (纸本)9789819976218

Sentiment Analysis is widely used in the process of mining the data, to predict emotion of a sentence through Natural Language Processing (NLP). The main aim is to find the accurate polarity of a sentence. Therefore, to find the polarity or sentiment of a user or customer for a product there is a need for automated data analysis techniques. In this paper, a detailed analysis of classification techniques is used in Sentimental Analysis of Amazon Product Reviews with recommendation for a best buy product on web. Multinomial Naïve Bayes, Random Forest, Logistic Regression, Decision Tree, and SVM classifiers are tested and compared. Random Forest gives the best accuracy of 94.94%. Web scraping extracts five Amazon products on *** and recommends the best buy product on the basis of polarity score of each product and here Samsung Galaxy M01 is recommended as the best buy product. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Classification Machine learning Polarity Sentimental analysis Web scraping

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Retrieval of Interactive Requirements of Data Intensive Applications using Random Forest Classifier

Informatica (Slovenia)

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Informatica (Slovenia) 2023年第9期47卷 35-50页

作者： Raymond, Renita Anouncia, S. Margret School of Computer Science and Engineering Vellore Institute of Technology Vellore India

Classifying requirements in data-intensive systems based on their interactions can assist the requirements engineering process in becoming more systematic and transparent, resulting in higher requirement compliance and software project completion. However, understanding the requirements centred on interactions with the system is particularly tough due to the increased complexity of big data. In most cases, awareness of interaction-based requirements is critical in moving forward with prediction and decision-making. As a result, the classification of interactive requirements plays a critical role in removing the difficulties from unclear requirements. Various approaches to effective requirement classification are being devised. However, due to inadequate requirement management reflecting the fast-changing organizational change, classification accuracy does not achieve its maximum potential. The best approach for reducing misclassification rate and retrieving interactive requirements for data-intensive systems would be to use Word Embedding and Random Forest Classifier retrieval mechanism, as none of the studies to date have emphasized it. It also assessed the impact by comparing the results to metrics derived from the Random Forest classifier's training on word count characteristics. The data set used to experiment with the classification, particularly for interaction-based needs, is unique to our work and has not been covered by any other studies to date. The researchers will benefit from this study as they will better understand the requirement classification process. With an F1 score of 0.91, precision of 0.89, and recall of 0.93, statistical analysis showed that Word Embedding followed by Random Forest Classifier produced a relatively high classification result to differentiate interactive requirements for data-intensive systems. © 2023 Slovene Society Informatika. All rights reserved.

关键词： Classification (of information)

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Artificial Intelligence Applications in Accounting and Finance

Artificial Intelligence Applications in Accounting and Finan...

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2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024

作者： Rahim, Rahila Chishti, Mohammad Ahsan National Institute of Technology Department of Computer Science Engineering Srinagar India

ISBN: (纸本)9798350372229

The world of Industry 4.0 embraces the transformation that calls for strategic investments, cooperative partnerships, and a proactive approach to overcome obstacles to successfully transition to smarter, more connected, and more efficient in- dustrial ecosystems. The accounting and finance industry has benefited greatly from process modernization, which has resulted in a significant decrease in the use of the traditional system through the integration of digital technologies. The use of various artificial intelligence technologies such as expert systems for audit and taxation, intelligent agents for customer service, Natural Language Processing (NLP), Robotic Process Automation (RPA), Blockchain, data analytics, and machine learning for decision- making has been proposed to automate, streamline, and improve the efficiency of various tasks and processes in accounting and finance operations. The study specifically investigates how the incorporation of AI technologies affects the reliability & timeliness of financial & accounting processes. The paper also presents some use cases and AI applications relevant to this field. © 2024 IEEE.

关键词： accounting AI finance ML NLP

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Summarizing Video Content with a Single Image Using Large Language Models 11

Summarizing Video Content with a Single Image Using Large La...

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11th International Conference on Advanced Informatics: Concept, Theory and Application, ICAICTA 2024

作者： Tamada, Shutaro Gu, Chunzhi Kuriyama, Shigeru Toyohashi University of Technology Department of Computer Science and Engineering Toyohashi Japan

ISBN: (纸本)9798331520311

Generating thumbnails for news videos plays an important role in efficiently understanding the contents. Prior techniques mostly handle this task by selecting one keyframe as a representative image. However, this approach cannot effectively handle a video whose key content is distributed across different frames. In this paper, we propose a summarization of a news video by composing its key contents into one image as a thumbnail. To achieve this, our method starts with text extraction from each scene in the video using OCR, speech recognition, and existing image captioning models. We then group these texts based on similarity and leverage large language models to score the group significance. Next, for each group, a keyframe is selected by jointly considering the importance and content quality. Eventually, we compose the objects in these keyframes into a single image as a thumbnail in a non-overlap manner and utilize diffusion-based generative models for further quality refinement. Experiments on real-world news videos demonstrate that our method can effectively extract key video contents and generate natural and informative video thumbnails. © 2024 IEEE.

关键词： Video analysis

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Parallel Momentum Methods Under Biased Gradient Estimations

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IEEE Transactions on Control of Network Systems 2025年第2期12卷 1721-1732页

作者： Beikmohammadi, Ali Khirirat, Sarit Magnusson, Sindri Stockholm University Department of Computer and System Science Stockholm11419 Sweden Thuwal Saudi Arabia

Parallel stochastic gradient methods are gaining prominence in solving large-scale machine learning problems that involve data distributed across multiple nodes. However, obtaining unbiased stochastic gradients, which have been the focus of most theoretical research, is challenging in many distributed machine learning applications. The gradient estimations easily become biased, for example, when gradients are compressed or clipped, when data is shuffled, and in meta-learning and reinforcement learning. In this work, we establish worst-case bounds on parallel momentum methods under biased gradient estimation on both general non-convex and μ-PL problems. Our analysis covers general distributed optimization problems, and we work out the implications for special cases where gradient estimates are biased, i.e. in meta-learning and when the gradients are compressed or clipped. Our numerical experiments verify our theoretical findings and show faster convergence performance of momentum methods than traditional biased gradient descent. © 2014 IEEE.

关键词： Adversarial machine learning

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Optimizing Energy Conservation in V2X Communications for 5G Networks

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computers, Materials & Continua 2022年第5期71卷 3479-3495页

作者： Arif Husen Abid Soahil Mohammad Hijji Muhammad Hasanain Chaudary Farooq Ahmed 无 Department of Computer Science COMSATS University IslamabadLahore CampusLahorePunjabPakistan Department of Computer Science and Information Technology Virtual University of PakistanLahorePunjabPakistan Computer Science Department University of TabukTabukSaudi Arabia

The smart vehicles are one of critical enablers for automated services in smart cities to provide intelligent transportation means without human *** order to fulfil requirements,Vehicle-to-Anything(V2X)communications aims to manage massive connectivity and high traffic load on base stations and extend the range over multiple hops in 5G ***,V2X networking faces several challenges from dynamic topology caused by high velocity of nodes and routing overhead that degrades the network performance and increases energy *** existing routing scheme for V2X networking lacks energy efficiency and scalability for high velocity nodes with dense *** order to handle the challenges,this article proposes a scalable and energy-efficient routing scheme called Dynamic proactive reactive routing for 5G(DPR5)for high mobility speed and dense *** compared to existing schemes it uses a single routing table and efficiently minimizes the energy consumption in dense environment,improves node‘s lifetime upto 42%,and optimizes network performance by reducing the packet loss ratio upto 46%in a high velocity dense environment.

关键词： Internet of vehicles vehicle-to-any device energy conservation

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