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International Conference on Computational Innovations and Engineering Sustainability (ICCIES)

作者： S. V. Vasantha M. Vineela Reddy S. Kruthika R. Sudhansh Mudhiraj Shanmugasundaram Hariharan Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India Department of Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9798331536695

ISBN: (纸本)9798331536701

Speech recognition through lip reading can help people with hearing loss, especially in noisy environments. Traditional communication tools are not designed to handle the needs of people who are deaf or hard to hear. This study developed a method that combines short term neural networks (LSTM) with convolutional neural networks (CNN) to record spontaneous words based on the appearance of lips. It involves preprocessing video frames, including resizing and filling with black frames when necessary, and using transformations to increase the predicting ability of the model. This model uses Time Distributed CNN layers and bidirectional LSTM layers to identify the time dependent physical properties of the image. This integration allows the system to correctly interpret and convert movement of lip into text. The model is trained using a custom data generator application that generates augmented data. The results of this study show significant improvements in lip reading accuracy upto 95%. People with hearing loss can independently understand more of the spoken content without the constant assistance of an interpreter or translator.

关键词： Visualization Accuracy Translation Lips Video sequences Auditory system Speech recognition data augmentation data models Convolutional neural networks

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Leveraging Deep Learning for the Identification and Categorization of Fruit Diseases

Leveraging Deep Learning for the Identification and Categori...

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Solanki, Shubhangi Singh Chouhan, Siddharth Dwivedi, Abhishek Singh, Uday Pratap Patel, Rajneesh Kumar SIT Lonavala SPPU Department of Computer Engineering 410401 India School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh Sehore Kothrikalan466114 India Manipal University Jaipur Department of Data Science and Engineering Rajasthan Jaipur303007 India Central University of Jammu Department of Mathematics 181143 India

ISBN: (纸本)9798350379525

The precise assessment of fruit quality and the detection of illnesses are critical tasks in the agriculture sector. One important component that has a direct impact on consumers' physical well-being and inclination to buy is the freshness of fruits and vegetables. Analyzing the degree of freshness in fruits and vegetables is important since it has a big impact on how market prices are set. Fruit and vegetable classification and identification by machine vision is difficult because of their comparable colors, textures, and external environmental elements including illumination, reflections, and complicated backdrops. This study presents a comprehensive overview of methods for detecting and classifying the quality of fruits and plants using several strategies for feature extraction and classification. Several academics have suggested various ways including binarization, histogram-based approaches, Gray Level Co-occurrence matrix, machine learning techniques, and deep learning. The outcome of our study showcases the results obtained from different methodologies, together with their respective benefits and challenges. In conclusion, we will discuss potential areas for improvement and future directions for additional research. © 2024 IEEE.

关键词： Fruits

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TransRec++:Translation-based sequential recommendation with heterogeneous feedback

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Frontiers of computer science 2022年第2期16卷 190-192页

作者： Zhuo-Xin ZHAN Ming-Kai HE Wei-Ke PAN Zhong MING National Engineering Laboratory for Big Data System Computing Technology Shenzhen UniversityShenzhen 518060China College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China

1 Introduction Nowadays,recommender systems are widely used because of the information overload problem on the *** are a variety of feedback in recommender systems,such as explicit feedback,implicit feedback,sequential feedback,*** them,modeling of heterogeneous sequential feedback,which contains not only different types of feedback such as examinations and purchases but also the sequential information,is an emerging and important problem receiving more and more *** sequential feedback is relatively easy to be collected in a deployed system and is also able to provide more information than the homogeneous feedback,which is thus expected to be helpful in improving the recommendation accuracy.

关键词： feedback recommendation sequential

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LLM2Vec Sentence Embeddings Analysis in Sentiment Classification

LLM2Vec Sentence Embeddings Analysis in Sentiment Classifica...

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International Conference on data and Software Engineering (ICODSE)

作者： Matthew Martianus Henry Nur Adhianti Hervanto Mahmud Isnan Dian Kurnianingrum Chyntia Ika Ratnapuri Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Entrepreneurship Department Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331506407

ISBN: (纸本)9798331506414

Sentiment analysis is crucial method in business intelligence to extract insights, which typically begin with sentiment classification. One of the latest frameworks for generating sentence embeddings for sentiment classification is LLM2Vec, which allows Transformer decoder-based models to generate sentence embeddings for text representation. Its capability is deemed language-agnostic, which, in this study, the framework is leveraged for Tokopedia tweet sentiment analysis to prove the claim. The base decoder models used in the LLM2Vec framework were Llama 3 8B, Llama 2 7B, Sheared Llama 1.3B, and Mistral 7B. Two BERT-based models, which are the Indonesian SBERT model and IndoBERT trained with the SimCSE approach, were employed as a comparison. The generated embeddings were classified using logistic regression, SVM, and MLP Classifier. Classifiers using embedding generated by LLM2Vec with Llama 3 8B and Mistral 7B achieves on-par performance with classifiers that utilize IndoBERT SimCSE embeddings, while classifiers using embeddings generated by LLM2Vec with Llama 2 7B and Sheared Llama 1.3B achieves much lower performance. Classifiers with Indonesian SBERT embeddings achieve the highest F1 score performance. Despite slightly lower performance, this study has proven the language-agnostic capability of LLM2Vec, especially with Llama 3 8B and Mistral 7B in colloquial Bahasa Indonesia sentiment analysis, since none of the base decoders were ever trained using the Bahasa Indonesia corpus.

关键词： Support vector machines Sentiment analysis Logistic regression Social networking (online) Blogs Transformers Encoding Decoding Business intelligence Software engineering

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Federated Opposite Learning Based Arithmetic Optimization Algorithm for Image Segmentation Using Multilevel Thresholding 26

Federated Opposite Learning Based Arithmetic Optimization Al...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Liu, Qingxin Qi, Qi Li, Ni Hainan University School of Computer Science and Technology Haikou570228 China Hainan Normal University School of Mathematics and Statistics Haikou571158 China Hainan Normal University Ministry of Education Key Laboratory of Data Science and Smart Education Haikou570228 China

ISBN: (纸本)9798350331684

Multilevel thresholding is a widely used method in image segmentation. However, the traditional methods are costly to obtain the optimal thresholds through exhaustive search. The Nature-inspired algorithm is a gradient-free optimizer that overcomes these shortcomings and generates the best thresholds with high quality and efficiency. For this purpose, this paper suggests an improved arithmetic optimization algorithm with federated opposite learning for multilevel thresholding image segmentation, namely FOL-AOA. In this method, the federated opposite learning strategy is incorporated to avoid the particles being trapped into local optimal and increase the population diversity. The cross-entropy is employed as the objective function minimized by FOL-AOA. To assess the performance of the proposed method, we considered the use of a variety of benchmark images under different threshold levels and compared them against five predecessor approaches. The obtained results manifest that FOL-AOA outperforms the comparison methods in terms of the fitness values as well as two image quality indicators such as PSNR and SSIM. © 2023 IEEE.

关键词： Image segmentation

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Improving Sentiment Classification on Restaurant Reviews Using Deep Learning Models 2

Improving Sentiment Classification on Restaurant Reviews Usi...

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2nd International Conference on Machine Learning and data Engineering, ICMLDE 2023

作者： Patil, Ratna Nitin Singh, Yadvendra Pratap Rawandale, Shitalkumar Adhar Singh, Sofia Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology Pune411048 India Computer Science and Engineering Department Manipal University Jaipur303007 India Department of Mechanical Engineering Pimpri Chinchwad College of Engineering and Technology Pune411044 India School of Engineering and Technology Amity University Noida201301 India

Natural Language processing (NLP) includes the task of classifying texts. The proposed paper uses the ability and power of machine learning and deep learning techniques for sentiment analysis on restaurant reviews. With the increasing use of social media, classification of reviews has significant role in the development of public opinion. This work has extensive applications from client opinion study, market research and social media monitoring. The machine learning algorithms and NLP techniques are used classifying reviews and the results are rigorously analysed. It was noted that results obtained from logistic regression and multinomial naïve bayes algorithms are better than other machine learning models. Hence these two algorithms are selected to optimize further. After tuning the hyperparameters by grid search technique, the best accuracy obtained by logistic regression and multinomial naive bayes on validation dataset was 89.9% and 89.6% respectively. Deep learning architectures were also utilized for reviews classification. The accuracy obtained by Convolutional Neural Net (CNN) and Bi-Directional Long-short-term-memory (Bi-LSTM) was 89% and 90% respectively. © 2024 Elsevier B.V.. All rights reserved.

关键词： Sentiment analysis

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Dynamic Searchable Scheme with Forward Privacy for Encrypted Document Similarity 22

Dynamic Searchable Scheme with Forward Privacy for Encrypted...

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22nd IEEE International Conference on Trust, Security and Privacy in Computing and Communications, TrustCom 2023

作者： Al Sibahee, Mustafa A. Luo, Chengwen Zhang, Jin Huang, Yijing Abduljabbar, Zaid Ameen Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Iraq University College Computer Technology Engineering Department Basrah61004 Iraq University of Basrah College of Education for Pure Sciences Department of Computer Science Basrah61004 Iraq Huazhong University of Science and Technology Shenzhen Institute Shenzhen518000 China

ISBN: (纸本)9798350381993

Document retrieval plays an essential role in many real-world applications especially when the data storage is outsourced. Due to the great advantages offered by cloud computing, clients tend to outsource their personal data to remote servers maintained by external service providers. This raises serious privacy concerns about outsourced data because such providers are usually considered untrusted entities. The majority of previous schemes of document similarity search share the same limitation: they focus mainly on static collections. Dynamic searchable schemes (DSE) allow adding or removing documents at the expense of more leakage than static schemes. To thwart certain attacks, DSE schemes should support forward privacy property, which ensures that newly added documents cannot be related to previously issued search queries. We design and implement dynamic secure similarity search schemes with forward privacy for textual documents utilizing simhash method for hamming similarity. Our scheme provides an efficient search time and a sufficient level of privacy. To show the practicality of our proposed scheme, we performed excremental results with large document collections. © 2023 IEEE.

关键词： Cryptography

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Salient Region Detection in Images Based on U-Net and Deep Learning 2

Salient Region Detection in Images Based on U-Net and Deep L...

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2nd IEEE International Conference on Advanced Technologies in Intelligent Control, Environment, Computing and Communication Engineering, ICATIECE 2022

作者： Kumar, Krishna Marimuthu, M. Das Gupta, Ayan Pant, Bhasker Shukla, Surendra Kumar Kapila, Dhiraj Bansal Institute of Engineering and Technology Department of Computer Science and Engineering Lucknow India Coimbatore Institute of Technology Department of Computing-Data Science Coimbatore India Chandernagore Government College Department of Geography Hugli India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Lovely Professional University Department of Computer Science and Engineering Phagwara India

ISBN: (纸本)9781665493963

Image analysis tasks use salient object detection because it not only identifies important elements of a visual scene but also lessens computational complexity by removing unimportant elements. In this research, we propose a novel salient object recognition method based on a deep learning network that maintains picture information in the mid and low regions. Using a deep learning model, our technique generates a coarse saliency map for the entire target image. The map is then fine-Tuned utilising low-To-mid level information particular to the image. For detection of salient objects, we use a U-Net as our architecture. The saliency map can be predicted pixel by pixel, reducing low-level visual information loss. Our results show that our system regularly outperforms other approaches for detecting salient objects, resulting in superior precision and recall rates. © 2022 IEEE.

关键词： Object detection

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Blockchain Based Voting System for Secure and Transparent Electoral Processes 10

Blockchain Based Voting System for Secure and Transparent El...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Sumathy, S. Murugan, S. Karthick Latha, J. Mageshwari, G. Andrews, Allen Joseph Gilbert Kumar, M. Hema Sir Theagaraya College-Shift Ii Pg Department of Computer Science Tamilnadu Chennai India R. M. D. Engineering College Department of Computer Science and Engineering Tamilnadu Thiruvallur India R. M. K. Engineering College Department of Computer Science and Engineering Tamilnadu Tiruvallur India R.M.K. College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamilnadu Tiruvallur India Deloitte consulting Llp 200 Sterling road MechanicsburgPA United States Sona College of Technology Department of Ece Tamil Nadu Salem India

ISBN: (纸本)9798350384369

This study describes an innovative approach to blockchain-based voting systems. The proposed method guarantees the integrity, security, and resistance to manipulation of elections conducted on a decentralized network through the utilization of smart contracts and cryptographic principles. Emphasizing participant confidentiality, vote integrity, and real-time auditability, the experimental outcomes demonstrate the system's scalability, dependability, and security. Within the domain of theoretical democratic discourse, presents a prospective scenario in which voting methods based on blockchain technology. The results indicate that the approach can be easily implemented on a large scale, representing a significant milestone in the struggle for democratic, secure, and transparent voting procedures. With highly efficient, boasting a transaction speed of 500 TPS, scalability to accommodate over a thousand users, and a block confirmation time of less than 10s. Encryption efficiency and digital signature dependability are indicators of the system's high level of security. Equipped with an accuracy rate that is 97.2%. © 2024 IEEE.

关键词： Decentralized finance

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Robust Intelligent System for COVID-19 Detection using CT-Scan

Robust Intelligent System for COVID-19 Detection using CT-Sc...

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2023 International Conference Automatics and Informatics, ICAI 2023

作者： Al Smadi, Ahmad Abugabah, Ahed Al-Smadi, Ahmad Mohammad Zarqa University Dep. of Data Science and Artificial Intelligence Zarqa13100 Jordan College of Technological Innovation Zayed University Abu Dhabi United Arab Emirates Al-Balqa Applied University Ajloun University College Department of Computer Science Jordan

ISBN: (纸本)9798350312911

In the beginning of 2020, the world witnessed the rapid spread of the new coronavirus, COVID-19, affecting millions of people globally. However, at the outset, the availability of corona test kits was scarce, leading researchers to explore alternative detection methods. Among these methods, the COVID-19 detection approach using CT-scans emerged, and artificial intelligence (AI)-based solutions proved to offer superior outcomes. Despite the potential of AI-based models, the issue of overfitting arose, significantly impacting model performance. In response to this challenge, we present a coherent and cohesive solution in this paper, utilizing a Convolutional Neural Network (CNN)-based approach for accurate classification of COVID-19 vs. non-COVID cases. To enhance the model's robustness, we incorporated data augmentation and batch normalization techniques for regularization. To evaluate the effectiveness of our proposed model, we conducted experiments with four different data splitting ratios (50%-50;70%-30;75%-25;80%-20) for training and testing. As a result, our suggested model achieved an impressive classification accuracy of 98.56% for distinguishing between COVID-19 and non-COVID cases. These promising results highlight the efficacy of our CNN-based approach with regularization techniques. Furthermore, we conducted a comparative analysis with other deep learning-based algorithms, and our model consistently outperformed them, demonstrating its superiority in COVID-19 detection. By providing such reliable and accurate results, our proposed model contributes significantly to the ongoing efforts in combating the COVID-19 pandemic and holds the potential to aid healthcare professionals in timely and precise diagnosis. © 2023 IEEE.

关键词： COVID-19

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