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6th Inter.a.ional Conference on computer Infor.a.ion Science and Application technology, CISAT 2023

作者： Haijing, Liu Department of Computer Engineering Taiyuan Institute of Technology Taiyuan China College of computing informatics and media University Technology Mara Malaysia

ISBN: (纸本)9781510668546

Text semantic similarity computation is a fundamental problem in the field of natural language processing. In recent years, text semantic similarity algorithms based on deep learning have become the mainstr.a. research paradigm, but they suffer from the problem of insufficient understanding of text semantics and thus unclear interpretation of computational results. In this paper, we propose a text semantic encoding model combining Frame semantic theory and deep learning, which enriches the semantic representation within sentences by making full use of the Frame, Frame Elements, lexical units, and inter-Frame relations in the Frame semantic knowledge base and combining them with the Bert model, and interacts with the semantics between sentences through Siamese Transformer encoders. The experiments are validated on MSr.a.aphraseCorpus and quora-question-pairs, and the results show that the model proposed in this paper outperforms similar models in terms of F1 values. © 2023 SPIE.

关键词： Semantics

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Modeling of Lycra/Cotton Core Spun Yarn for Strength and Elastic recovery Using Adaptive Neuro-Fuzzy Inference System (ANFIS)

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Jour.a. of The Institution of Engineers (India): Series E 2024年第1期105卷 67-75页

作者： Nurussaba, Kasimi Sinha, Avishek Bhatia, Dinesh Sachdeva, Anish Sinha, S.K. Department of Textile Technology Dr. B.R. Ambedkar National Institute of Technology Punjab Jalandhar India Department of Computer Science and Engineering Dr. B.R. Ambedkar National Institute of Technology Punjab Jalandhar India Department of Textile Engineering Jawahar Lal Nehru Govt. Engineering College Sundernagar Himachal Pradesh Sundernagar India Department of Industrial and Production Engineering Dr. B.R. Ambedkar National Institute of Technology Punjab Jalandhar India

The main goal of using core yarn is to take benefit from the various qualities of its components. The filament used in core yarn adds strength to the yarn, while the sheath fibers gives the appearance and physical attributes. As the property of yarn is influenced by the core, the var.a.ion of core provides option for widening application of core spun yarn. In recent days, stretch denim fabrics have raised the potential application of denim fabric in apparel sector. The process parameters influence the structure and ultimately the behavior of the yarn and that of the product made out of it. Optimization of the parameters can help in economic production of the best quality of product using available resources. In our study process parameters such as linear density, stretch percentage of lycra and testing parameters such as gauge length and speed of testing are optimized to get high elastic recovery and tenacity of yarn. The error percentage for optimized results is not more than 7 percent so model is valid for.a.tual usage. © The Institution of Engineers (India) 2024.

关键词： Yarn

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Sentiment Analysis of Game reviews on STEAM using BErT, BiLSTM, and CrF

Sentiment Analysis of Game Reviews on STEAM using BERT, BiLS...

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2023 Inter.a.ional Conference on Electrical engineering and informatics, ICEEI 2023

作者： Al Mursyidy Fadhlur.a.man, Jalaluddin Herawati, Neng Ayu Widya Aulya, Hayyu rachma Puspasari, Ira Utama, Nugraha Priya School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Dinamika University Computer Engineering Department Surabaya Indonesia

ISBN: (纸本)9798350381290

The Steam platform releases thousands of games almost every week, providing customers with many options, which require various considerations to make a purchase decision. Steam has features that can help consumers consider purchasing games, such as the help dashboard that provides infor.a.ion on game reviews and ratings. However, based on user input reviews, game ratings are still manual, which may cause human error.a.d inaccurate results. To help gamers get the desired game infor.a.ion and incr.a.e customer purchasing power towards available games, sentiment analysis is one technique for classifying opinions. This study focuses on sentiment analysis of game reviews, which will result in classification into two classes, positive and negative. It will be measured for its perfor.a.ce based on the f1 score evaluation results. The proposed model that we cr.a.ed is to compare sentiment analysis models using the baseline Bi-directional Long-short Term Memory (BiLSTM), Bidirectional Gated recurrent Unit (BiGrU), and fine-tuning Bidirectional Encoder representations from Transformer (BErT). We then propose another model, BiLSTM-CrF, by examining the effect of using the last four hidden states of BErT and comparing it with the last one hidden state of BErT to be used in the BiLSTM-CrF model. Based on the results of sentiment analysis models we developed for the ten most reviewed games on Steam, the BiLSTM model combined with CrF on the last four hidden states yielded the best perfor.a.ce, with an Fl score of 95.2 %. This score exceeds the baseline transformer-based model's Fl score of 88.0%. This is because combining BiLSTM and CrF allows the log-likelihood of the BiLSTM emission sample to be obtained more accurately so the model does not lose its context. © 2023 IEEE.

关键词： Sentiment analysis

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Classification of Tuna Meat Grade Quality Based on Color Space Using Wavelet and k-Nearest Neighbor.a.gorithm

Classification of Tuna Meat Grade Quality Based on Color Spa...

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2023 Inter.a.ional Conference on Smart-Green technology in Electrical and Infor.a.ion Systems, ICSGTEIS 2023

作者： Gede Sujana Eka Putra, I. Ketut Gede Dar.a.Putra, I. Sudar.a. Made Sudana, Anak Agung Kompiang Oka Institute of Business and Technology Indonesia Department of Computer Informatics Denpasar Indonesia Udayana University Department of Information Technology Denpasar Indonesia Udayana University Department of Electrical and Computer Engineering Denpasar Indonesia

ISBN: (纸本)9798350382822

Tuna products are one of Indonesia's leading export commodity products. Accuracy in determining the quality grade of tuna is necessary to ensure food safety and product quality. Several cases of rejection of Indonesian fishery products by the United States and cases of food poisoning show a lack of food safety management in Indonesia. Determination of fish quality grade was done manually by identifying the eyes condition, gills, and meat color, taking a sample of tuna meat using a spike, which is the manual grading process causing human error. Previous studies had been carried out to identify the freshness and quality of fish using odor profiles, and color profiles from color sensors, measuring fish freshness and quality based on fish eye images. According to robert DiGregorio, five parameters determine the grade quality of tuna, namely freshness, fish size and shape, meat color, texture, and fat content. Fish grades are grouped into grade 1, grade 2+, grade 2, and grade 3. This study aims to determine the grade quality of tuna meat based on color space through image preprocessing, dataset training, and classification. Image preprocessing consists of image cropping, converting images from rGB to HSV, and feature extraction using a wavelet. The training phase uses the k-NN algorithm using k=4 based on the number of classes. The result shows the correlation coefficient between grades of feature extraction using wavelet Symlet better than Haar. Classification of 65 images test dataset using Symlet wavelet and k-NN has a better.a.curacy of 81.8% compared to the Haar wavelet k-NN with an accuracy of 80.3%. © 2023 IEEE.

关键词： Feature extraction

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Big Five Personality Prediction Based on Indonesian Tweets and Personality Test

Big Five Personality Prediction Based on Indonesian Tweets a...

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2023 Inter.a.ional Conference on Electrical engineering and informatics, ICEEI 2023

作者： Parameswara, Made Arbi Mahendra, Farrel Satya Putra Mardika, Muhammad randy Cahya Puspasari, Ira Utama, Nugraha Priya School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Dinamika University Computer Engineering Department Surabaya Indonesia

ISBN: (纸本)9798350381290

The integration of human resources and artificial intelligence has shown a growing interest in automatically predicting personality traits, particularly in recruitment. To contribute to this field, we propose a new method to assist human resource professionals in Indonesia to identify candidates' personality traits. Biases come from personality tests due to their question for.a. and susceptibility to manipulation on this research, we propose an appr.a.h that combines personality tests using the BFI form, which measures OCEAN personality traits, with personality trait predictions derived from Twitter data. This research aims to cr.a.e a personality prediction model and system that efficiently and effectively screens candidate personality traits in human resources. This research has developed a novel model that combines personality test results with tweets personality prediction with an impressive F1-macro perfor.a.ce of 98%. © 2023 IEEE.

关键词： Machine learning

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Chaotic Hippopotamus based Energy Efficient routing for Wireless Sensor Networks 4

Chaotic Hippopotamus based Energy Efficient Routing for Wire...

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4th Inter.a.ional Conference on Ubiquitous Computing and Intelligent Infor.a.ion Systems, ICUIS 2024

作者： Vedanarayanan, V. Jayaraman, Venkatesh Lakshmi, r.T.Thivya Surendran, r. Sathyabama Institute of science and technology Department of Electronics and Communication Engineering Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr.Sangunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331529635

The energy efficient routing in wireless sensor network (WSN) is crucial for enhancing the network longevity. For this, energy efficient routing protocol is introduced with cluster based routing. Initially, the sensor nodes are deployed randomly in the network ar.a.and are clustered together using the Chaotic Hippopotamus Optimization (ChHipO) Algorithm. The algorithm considers remaining energy, delay and distance as the multi-objective fitness factor for choosing the cluster head (CH) using the ChHipO Algorithm. The proposed ChHipO Algorithm integrates iterative chaotic mapping in the hippopotamus optimization algorithm for improving the rate of convergence. The analysis of ChHipO Algorithm based on residual energy, network lifetime, throughput and end-to-end delay acquired the values of 0.87, 99.728, 99.589 and 3.01 respectively. © 2024 IEEE.

关键词： Sensor nodes

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Facial emotion recognition using convolutional neural networks

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Mater.a.s Today: Proceedings 2023年 80卷 3560-3564页

作者： Sar.a.ar, Ketan Senkamalavalli, r. raghavendra, S. Santosh Kumar, J. Manjunath, r. Jaiswal, Sushma Information Technology Department U.V. Patel College of Engineering Ganpat University Gujarat Mehsana384012 India CSE Department East Point College of Engineering Bangalore India Department of Computer Science and Engineering School of Engineering and Technology Christ Deemed to be University Bengaluru560074 India Department of Computer Science and Engineering K S School of Engineering and Management Bengaluru56019 India Department of Computer Science and Engineering R R Institute of Technology Bengaluru560090 India Bilaspur495009 India

Emotional expressivity has always been a simple job for people, but computer programming is much harder to accomplish. Image emotions may be recognised by recent developments in computer vision and machine learning. In this article, we present a new method to detect face emotion. Use neural networks convolutionary (FErC). The FErC is based on a CNN network of two parts: the first portion removed the backdrop of the image, the second part removed the face vector. The expressional vector (EV) is utilised in the FErC model to detect the fve different kinds of regular facial expressions. The double-level CNN is continuous and the weights and exponent values of the final perception layer vary with each iteration. In that it improves accuracy, FErC varies from widely utilised CNN single-level technology. Moreover, EV generation prevents the development of a number of issues before a new background removal process is used (for example distance from the camera). © 2021

关键词： Emotion recognition

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AI-Enhanced Farming: A r.a.-Time Monitoring App powered by IoT 5

AI-Enhanced Farming: A Real-Time Monitoring App powered by I...

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5th Inter.a.ional Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Gayathri, r. r.a., r. Sathesh Balaanand, M. Kumar, J. Arun Sasikala, N. rajesh, K. Psna College of Engineering and Technology Department of Computer Science and Engineering Dindigul India Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Tagore Institute of Engineering & Technology Department of Computer Science and Engineering Salem India Dayananda Sagar University Department of Computer Science and Engineering Karnataka Harohalli India Ssm Institute of Science and Technology Department of Electronics and Communication Engineering Dindigul India

ISBN: (纸本)9798331504403

The Constant challenge faced by the farmers is to maintain their crops and enhance productivity. Despite various methods and solution evolving, still farmers can't upgrade to it. Farmers are not aware of the modern agricultural methods. The major cause is that the software and solution developed are of higher complexity. To upgrade them, technology is the foremost impor.a.t tool to stand up in this modern world. To help them with their problem and adapt to the modern world, the development of software with inbuilt AI and user-friendly features (in agriculture) is needed. This study proposes a novel Agri-tech app, which is a IoT based app used by the farmers for weather forecasting, soil moisture and nutrient content analysis. The developed application also recommends the crop type that can be cultivated in the soil by using the database of soil moisture and nutrient content. IoT sensors fixed in the field are used to detect the moisture level in the soil and notify the farmers to irrigate the land. It recommends the farmer to add manures and fertilizers to soil by calculating the amount of nitrogen, phosphorous, and oxygen contents present in the soil. Using these technical innovation farmers can monitor their fields over.a.system or smart phone. Moreover, the proposed appr.a.h can promote crop diversity and rotation for healthier soil, encourage sustainable practices like or.a.ic farming. © 2024 IEEE.

关键词： Soil moisture

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Awake at the Wheel: Enhancing Automotive Safety Through EEG-Based Fatigue Detection 27th

Awake at the Wheel: Enhancing Automotive Safety Through EEG-...

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27th Inter.a.ional Conference on Pattern recognition, ICPr 2024

作者： Siddhad, Gourav Dey, Sayantan roy, Partha Pratim Iwamura, Masakazu Department of Computer Science and Engineering Indian Institute of Technology Roorkee Uttarakhand Roorkee247667 India Department of Core Informatics Graduate School of Informatics Osaka Metropolitan University Osaka Sakai599-8531 Japan

ISBN: (纸本)9783031781940

Driver fatigue detection is incr.a.ingly recognized as critical for enhancing r.a. safety. This study introduces a method for detecting driver fatigue using the SEED-VIG dataset, a well-established benchmark in EEG-based vigilance analysis. By employing advanced pattern recognition technologies, including machine learning and deep neural networks, EEG signals are meticulously analyzed to discern patterns indicative of fatigue. This methodology combines feature extraction with a classification framework to improve the accuracy of fatigue detection. The proposed NLMDA-Net r.a.hed an impressive accuracy of 83.71% in detecting fatigue from EEG signals by incorporating two novel attention modules designed specifically for EEG signals, the channel and depth attention modules. NLMDA-Net effectively integrate features from multiple dimensions, resulting in improved classification perfor.a.ce. This success stems from integrating temporal convolutions and attention mechanisms, which effectively interpret EEG data. Designed to capture both temporal and spatial characteristics of EEG signals, deep learning classifiers have proven superior to traditional methods. The results of this study reveal a substantial enhancement in detection rates over existing models, highlighting the efficacy of the proposed appr.a.h for practical applications. The implications of this research are profound, extending beyond academic r.a.ms to inform the development of more sophisticated driver.a.sistance systems. Incorporating this fatigue detection algorithm into these systems could significantly reduce fatigue-related incidents on the r.a., thus fostering safer driving conditions. This paper provides an exhaustive analysis of the dataset, methods employed, results obtained, and the potential r.a.-world applications of the findings, aiming to contribute significantly to advancements in automotive safety. © The Author(s), under exclusive license to Springer Nature Switzer.a.d AG 2025.

关键词： Deep neural networks

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Comparative Analysis of Movie recommendation Systems Using Filtering Techniques on IMDB and rotten Tomatoes 15

Comparative Analysis of Movie Recommendation Systems Using F...

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15th Inter.a.ional Conference on Infor.a.ion, Intelligence, Systems and Applications, IISA 2024

作者： Majhi, Santosh Kumar Sahani, Preetipadma Pradhan, rosy Achar.a. Biswaranjan Grivokostopoulou, Foteini Kanavos, Andr.a. Gerogiannis, Vassilis C. Department of Computer Science and Information Technology Bilaspur India Veer Surendra Sai University of Technology Department of Computer Science and Engineering Odisha Burla India Veer Surendra Sai University of Technology Department of Electrical Engineering Odisha Burla India Marwadi University Department of Computer Engineering Ai Rajkot India Computer Technology Institute and Press 'Diophantus' Patras Greece Ionian University Department of Informatics Corfu Greece University of Thessaly Department of Digital Systems Larissa Greece

ISBN: (纸本)9798350368833

recommendation systems are pivotal in industries like enter.a.nment, where competitiveness is intense. This study enhances movie recommendations by utilizing rich datasets from IMDB and rotten Tomatoes, incorporating movie ratings, user profiles, audience scores, and critics' reviews. These elements were subjected to sentiment analysis to refine the data quality. We explored three filtering techniques: content-based, which analyzes movie features;collaborative, which processes user behavior patterns;and hybrid, combining both to leverage their strengths. The effectiveness of these methods was assessed using Mean Average Precision at k (MAP-k) and root Mean Square Error (rMSE), with our results showing notable improvements in recommendation accuracy. For instance, the hybrid method achieved a MAP ©k of 0.271 and an rMSE of 0.634, indicating enhanced precision and reliability of the recommendations. These findings suggest significant potential for improving user satisfaction and retention in digital platforms, demonstrating the benefit of integrating diverse data sources and analytical techniques in recommendation systems.

关键词： Collaborative filtering

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