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2023 International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2023

作者： Narkhede, Nandkishor mathur, Sumit Bhaskar, Anandkumar Atalbihari Hiran, Kamal Kant Dadhich, Manish Kalla, Mukesh Shah and Anchor Kutchhi Engineering College Electronics and Computer Science Department Mumbai India Madhav University Facuilty of Engineering and Technology Sirohi India Sir Padampat Singhania University Electronics and Communication Department Udaipur India School of Computer Science & It Symbiosis University of Applied Sciences Indore India School of Management Sir Padampat Singhania University Udaipur India Sir Padampat Singhania University Computer Science and Engineering Department Udaipur India

ISBN: (纸本)9798350314878

To tackle difficulties such as finding similar songs, identifying cultures that would enjoy certain music, conducting surveys and music therapy, Classification and Recognition of Music Genre is necessary. The expansion of music-related content available online has created new opportunities for the development of music referral systems, which support user communities in searching, finding, sharing, and producing digitally. We develop a new methodical perspective to select the most optimum machine-learning algorithm for predicting song genre using Support Vector Machine (SVM), k-nearest neighbor (k-NN), Probabilistic Neural Network (PNN) and Convolutional Neural Network. We got the results for CNN having system's accuracy level is 98.9% and a precision of 98.7%, f1 score of 97.5%, recall of 98.5%. and the accuracy level of the system is 98.3% from SVM, 98.5% from PNN, 99.9% from KNN. © 2023 IEEE.

关键词： Nearest neighbor search

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DL-CMFD: Deep Learning-Based Copy-Move Forgery Detection Using Parallel Feature-Extractor

DL-CMFD: Deep Learning-Based Copy-Move Forgery Detection Usi...

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2023 IEEE International Conference on computer, Information, and Telecommunication Systems, CITS 2023

作者： Faheem, Layeba Mukherjee, Soumya Obaidat, Mohammad S. Pal, Arup Kumar Islam, Sk Hafizul Sadoun, Balqies Dhanbad Department of Computer Science and Engineering Jharkhand Dhanbad826004 India University of Science and Technology Beijing100083 China Amity University UP Noida201313 India Indian Institute of Information Technology Department of Computer Science and Engineering West Bengal Kalyani741235 India College of Engineering Al-Balqa' Applied University Al-Salt19117 Jordan

ISBN: (纸本)9798350336092

Due to readily available software, anyone can easily manipulate the image and create a piece of false information. Among different types of image forgery, copy-move image forgery is widely practiced due to its sophistication and difficulty distinguishing it from the original image. We proposed a new deep learning-based copy-move forgery detection (DL-CMFD) technique to identify the forged image by successfully localizing the forged area to address this challenge. To attain this, the scale-invariant features are extracted from the forged image using the parallel feature extraction technique utilizing a batch normalization-based convolution neural network with the aid of the ReLU activation function. In the next step, to identify the identical features, self-correlation has been calculated, and further, percentile pooling is used to find the top k-percent features. Lastly, a masked image is produced as the output. The proposed scheme has been deployed on the different available datasets like CoMoFD and COCO to testify to the efficiency and accuracy. The proposed scheme is found to be robust because it can detect the forged area accurately when deployed on post-processed forged images. We made various statistical parametric evaluations to evaluate the efficacy and conformity of the proposed copy-move forgery detection scheme, and a comparison analysis is also made. © 2023 IEEE.

关键词： Convolutional neural networks

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Enhancing Urban Air Quality with Smart Vehicle Exhaust Management

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Operations Research Forum 2025年第2期6卷 1-24页

作者： Tyagi, Vaishali Dubey, Roshan Singh, Suraj Ram, Mangey Department of Applied Sciences & Humanities ABES Institute of Technology Uttar Pradesh Ghaziabad India Department of Computer Science & Engineering ABES Institute of Technology Uttar Pradesh Ghaziabad India Department of Mathematics Computer Science and Engineering Graphic Era Deemed to be University Uttarakhand Dehradun India

Air pollution is the contamination of the indoor or outdoor environment by any chemical, physical, or biological agent that modifies the natural characteristics of the atmosphere. Burning gasoline and diesel fuel creates harmful byproducts like nitrogen dioxide, carbon monoxide, hydrocarbons, benzene, and formaldehyde. This study proposes a sensor-based model to control vehicular pollution by monitoring emission levels and preventing vehicles from operating if their pollution exceeds government standards. The proposed model consists of a smoke sensor, particulate sensors, carbon monoxide sensors, along with Arduino based microcontroller, data logger, LCD display, and GPRS modem. Using the block diagram, a mathematical model has been developed and some differential equations by using Markov process have been generated. This Markov-based model gives the information about the failure causes and working of the proposed system. Some reliability matrices like availability, mean time between failure (MTBF), and sensitivity analysis have been evaluated with the help of Laplace transform and illustrated with the help of graphs. Also, the failure cases of the system and its cost analysis have been done along with all the numerical calculations required for the successful operation of the model. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Air pollution Cost analysis Failure cases Markov process Reliability Sensor system

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A Virtual Assistance for Visually Impaired People to Recognize Fabric Pattern and Color Using Human computer Interaction Logic 9

A Virtual Assistance for Visually Impaired People to Recogni...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Panicker, M.S Bravishma Mohandas, R. Hariharan, Shanmugasundaram Devi, K. Nirmala Chaitanya, M.S.K. Mishra, Rashmi Tamil Nadu Chennai77 India Chennai Institute of Technology Department of Ece Chennai India Vardhaman College of Engineering Department of Artificial Intelligence and Data Science India Madanapalle Institute of Technology & Science Department of Computer Applications India S.R.K.R Engineering College Department of Civil chinamiram Andra pradesh Bhimavaram India G L Bajaj Institute of Technology & Management Department of Applied Science & Humanities Greater Noida India

ISBN: (纸本)9798350377972

Modern advancements in technology have paved the way for innovative solutions to assist visually impaired individuals in their daily lives. This study presents a virtual assistance system designed to recognize fabric patterns and colors using a combination of deep learning and human-computer interaction techniques. The system employs high-quality image acquisition methods, bilateral filtering, and histogram equalization to preprocess images, enhancing their quality for subsequent analysis. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are integrated to form a Hybrid Fabric Pattern Recognition Network (HFPRN) that accurately identifies fabric patterns. Additionally, the system utilizes RGB color space conversion for precise color detection. Voice command capabilities via Amazon Alexa and tactile feedback mechanisms further enhance user interaction. Experimental results demonstrate the proposed system's high accuracy, with the HFPRN achieving 97% accuracy in fabric pattern recognition and 95% accuracy in color detection. This comprehensive solution significantly improves the autonomy and quality of life for visually impaired individuals by providing real-time, accurate feedback on fabric patterns and colors. © 2024 IEEE.

关键词： Convolutional neural networks

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Improving Aspect-Based Sentiment with End-to-End Semantic Role Labeling Model

Improving Aspect-Based Sentiment with End-to-End Semantic Ro...

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2023 International Conference Recent Advances in Natural Language Processing: Large Language Models for Natural Language Processing, RANLP 2023

作者： Přibáň, Pavel Pražák, Ondrvej Department of Computer Science and Engineering University of West Bohemia Faculty of Applied Sciences Czech Republic NTIS - New Technologies for the Information Society University of West Bohemia Faculty of Applied Sciences Czech Republic

ISBN: (纸本)9789544520922

This paper presents a series of approaches aimed at enhancing the performance of Aspect-Based Sentiment Analysis (ABSA) by utilizing extracted semantic information from a Semantic Role Labeling (SRL) model. We propose a novel end-to-end Semantic Role Labeling model that effectively captures most of the structured semantic information within the Transformer hidden state. We believe that this end-to-end model is well-suited for our newly proposed models that incorporate semantic information. We evaluate the proposed models in two languages, English and Czech, employing ELECTRA-small models. Our combined models improve ABSA performance in both languages. Moreover, we achieved new stateof-the-art results on the Czech ABSA. © 2023 Incoma Ltd. All rights reserved.

关键词： Sentiment analysis

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Multilingual Hate Speech Detection: Innovations in Optimized Deep Learning for English and Arabic Hate Speech Detection

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SN computer science 2025年第3期6卷 1-16页

作者： AL-Sukhani, Hassan Bsoul, Qusay Elhawary, Abdelrahman H. Nasr, Ziad M. Mansour, Ahmed E. Batyha, Radwan M. Alqadi, Basma S. Alqurni, Jehad Saad Alfagham, Hayat Madbouly, Magda M. Cybersecurity Department Faculty of Science and Information Technology Jadara University Irbid Jordan Cybersecurity Department College of Computer Sciences and Informatics Amman Arab University Amman Jordan Faculty of Engineering Ain Shams University Cairo Egypt Faculty of Computer Science Misr International University Cairo Egypt Faculty of Information Technology Applied Science Private University Amman 11931 Jordan Computer Science Department College of Computer and Information Sciences Al Imam Mohammad Ibn Saud Islamic University Riyadh Saudi Arabia Department of Educational Technologies College of Education Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam Saudi Arabia Department of MIS College of Applied Studies and Community Service Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam Saudi Arabia Institute of Graduate Studies & Research Alexandria University Alexandria Egypt Faculty of Computer and Data Science Alexandria University Alexandria Egypt

This paper presents the development of a multilingual hate speech detection model that effectively processes and classifies content in both Arabic and English. The study leverages both traditional machine learning models, such as K-Nearest Neighbors (KNN), Naive Bayes, and Support Vector Machines (SVM), as well as advanced deep learning models, specifically Bi-directional Long Short-Term Memory (Bi-LSTM) networks. A key challenge addressed is the classification of mixed-language content, which is common on social media platforms in the MENA region. To enhance detection performance, preprocessing techniques were applied to the text data, and the Synthetic Minority Over-sampling Technique (SMOTE) was used to balance the dataset. The results show that the Bi-LSTM model outperformed traditional machine learning approaches, particularly in identifying hate speech across multiple languages. The proposed model demonstrates superior accuracy and robustness in handling mixed-language input, providing a more effective solution for real-world hate speech detection tasks. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Class imbalance Deep learning Hate speech detection Machine learning Mixed Arabic and English training Multilingual NLP Natural language processing (NLP) Sentiment analysis

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Significance of Internet-of-Things Edge and Fog Computing in Education Sector

Significance of Internet-of-Things Edge and Fog Computing in...

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2023 International Conference on Smart Computing and Application, ICSCA 2023

作者： Siddiqui, Shams Tabrez Khan, Mohammad Rafeek Khan, Zeba Rana, Nadim Khan, Haneef Alam, Md Imran College of Cs It Jazan University Department of Computer Science Jazan Saudi Arabia College of Cs It Jazan University Department of Computer and Network Engineering Jazan Saudi Arabia Computer and Information System Applied College Jazan University Jazan Saudi Arabia College of Computer Science and Information Technology Jazan University Jazan Saudi Arabia

ISBN: (纸本)9798350347050

IoT gadgets include smart lights, ovens, and industrial data-collecting equipment. IDC expects 41.6 billion connected IoT devices to generate 79.4 ZB of data by 2025. Early IoT devices sent data to the cloud for analysis. When sending trillions of gigabytes to the cloud, the data pipeline clogs. Edge computing enables IoT devices to handle data locally instead of sending it to the cloud. Data is processed at the 'edge' of your local network, rather than transferred away. Technology's versatility makes it valuable in education. IoT edge computing has different requirements than non-IoT edge computing. IoT devices have limited data processing and storage capabilities, thus processing must occur off the device. The edge offers an environment to manage huge numbers of IoT devices and data. Many functions can be off-loaded to the edge, reducing device cost. In education, IoT edge computing architecture has gained importance due to real-time applications. In order to facilitate the analysis of educational system architecture, this paper seeks to provide an IoT Edge computing framework in the field of education. The framework will identify IoT-related issues and concerns. This study discusses IoT Edge computing in education, identifies limits, and makes recommendations. As well as the paper evaluates IoT Edge computing's performance in educational systems. © 2023 IEEE.

关键词： Edge computing

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Prediction of Metabolic Syndrome based on Non-invasive Measurement Features for Chronic Disease Management 22

Prediction of Metabolic Syndrome based on Non-invasive Measu...

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8th International Conference on computer Technology Applications, ICCTA 2022

作者： Shin, Hyunseok Shim, Simon Choo, Charles Hwang, Doosung Nah, Yunmook Oh, Sejong Department of Computer Science Dankook University Korea Republic of Department of Computer Engineering San Jose State University United States Department of Electrical Engineering San Jose State University United States Department of Software Science Dankook University Korea Republic of Department of Applied Computer Engineering Dankook University Korea Republic of

ISBN: (纸本)9781450396226

Metabolic syndrome is a chronic disease in which metabolism-related abnormal factors are complex. Metabolic syndrome is currently diagnosed by determining the number of abnormal factors based on physical measurements and blood tests. Metabolic syndrome causes complications in patients with diabetes or the cardiovascular system;therefore, prevention and management are particularly important. Metabolic syndrome can be prevented effectively by allowing individuals to manage it themselves;however, blood tests are a major obstacle to the public. Therefore, this study is conducted to devise a method that can easily predict metabolic syndrome without requiring a blood test. A dataset containing data of 69,944 adult Korean men and women is used to develop a predictive model. This dataset contains not only physical measurements and blood test results, but also life logs pertaining to diet, food intake, drinking, and smoking. Using these data, we identify features that contribute significantly to the prediction of metabolic syndrome, except for items associated with blood test. Finally, we propose a predictive model that allows the public to easily manage metabolic syndrome using only non-invasive factors. Furthermore, we investigate methods to improve the predictive performance from the perspective of four subgroups, based on waist circumference and blood pressure. © 2022 ACM.

关键词： Forecasting

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Exact Output Synchronization of Discrete-Time Multi-Agent Systems in the Presence of Disturbances and Measurement Noise with Known Frequencies via Scale-Free Protocol Design 42

Exact Output Synchronization of Discrete-Time Multi-Agent Sy...

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42nd Chinese Control Conference, CCC 2023

作者： Liu, Zhenwei Zhang, Meirong Saberi, Ali Stoorvogel, Anton A. College of Information Science and Engineering Northeastern University Shenyang110819 China School of Engineering and Applied Science Gonzaga University SpokaneWA United States School of Electrical Engineering and Computer Science Washington State University PullmanWA99164 United States Mathematics and Computer Science Department of Electrical Engineering University of Twente Enschede Netherlands

ISBN: (纸本)9789887581543

This paper aims to achieve scalable exact output synchronization for discrete-time homogeneous multi-agent systems in the presence of disturbances and measurement noise with known frequencies. The key contribution of this paper is on the distributed protocol that only uses the information of agent models, rather than the communication network information and the agent number, so as to achieve the scalable exact synchronization under disturbances and measurement noise. The validity of the protocol is verified by numerical simulations with arbitrarily chosen number of agents. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Multi agent systems

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Development and Verification of a Microservice Architecture for a Fire Risk Notification System 43rd

Development and Verification of a Microservice Architectu...

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43rd International Conference on Application and Theory of Petri Nets and Concurrency , Petri Nets 2022

作者： Strand, Ruben Dobler Kristensen, Lars M. Petrucci, Laure Department of Safety Chemistry and Biomedical Laboratory Sciences Western Norway University of Applied Sciences Haugesund Norway Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway LIPN CNRS UMR 7030 Université Sorbonne Paris Nord Villetaneuse France

ISBN: (纸本)9783662681909

Long periods of dry and cold weather conditions significantly increase fire risks for wooden buildings. Recent advances in predictive fire risk models combined with publicly available cloud-based weather data services have enabled the development of smart software systems for location-oriented fire risk notification. We have developed a Coloured Petri Net (CPN) model specifying the software architecture of a microservice-based predictive fire risk notification system. The CPN model captures the set of micro-services provided via REST APIs and the interaction between the constituent services for location tracking and subscription, fire risk computation and data harvesting. As part of the work, we have applied an agile-oriented implementation-first modelling approach and developed a general pattern for REST-based APIs. We apply simulation and state space exploration to validate and verify key behavioural properties of the predictive fire risk notification system. © 2024, The Author(s), under exclusive license to Springer-Verlag GmbH, DE, part of Springer Nature.

关键词： Software architecture

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