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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Unnikrishna, Pillai Midhun Pulkuzhiyil, Vinayak Krishna, Jyothika Wilson, Naveen Kumar, Veena A. Saintgits College Of Engineering Dept. of Computer Science and Engineering Kottayam India

ISBN: (纸本)9798350381689

An extensive use case of Generative Adversarial Networks (GANs) that not only combines artistic creativity with technological innovation but also significantly improves identification precision, filling in critical gaps in the smooth integration of textual descriptions with visual representations. The motivation is rooted on the difficulties that currently exist in accurately identifying human face sketches by directly transforming them into images, an area where prior approaches were inadequate. Our special dual-function technology is designed to get beyond these obstacles and provides a fresh approach for forensic investigators, artists, and sculptors. It has the ability to convert text to image as well as sketch to image. This easy transformation of textual and drawing data represents an integrated combination of creativity and technology that is set to have an impact on a number of disciplines, such as suspect identification and crime scene reconstruction. © 2024 IEEE.

关键词： Generative adversarial networks

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Enhanced Text-Summarization and Language Simplification:End-to-End Approach for Content Comprehension 1

Enhanced Text-Summarization and Language Simplification:End-...

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1st International Conference on Trends in engineering Systems and Technologies, ICTEST 2024

作者： Riza Ayisha, P. Mathew, George Government Engineering College Dept. of Computer Science and Engineering Thrissur India

ISBN: (纸本)9798350307795

Text summarization is the process of shortening a large body of text in a document to form a shorter while preserving its most important information. With the exponential growth of digital data, there is a need for automated text summarization to help users quickly comprehend the content. The use of machine learning techniques, particularly deep learning models, has shown great promise in generating high-quality summaries. Among these models, BERT (Bidirectional Encoder Representations from Transformers) has emerged as a state-of-the-art method for various natural language processing (NLP) tasks, including text summarization. In this paper, we propose to implement a text summarization system using the BERT model. Apart from the normal text summarization, we will be implementing a Language Simplification model that simplifies complex sentence structures or use simpler vocabulary while preserving the original meaning. This model transforms convoluted or verbose sentences into shorter and more straight-forward constructions The paper aims to develop an end-to- end summarization system that takes a long document as input and generates a summary of the most important information contained in the document. For example, like taking the entire news from an online portal and generates it summary. © 2024 IEEE.

关键词： Signal encoding

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A Study of Various Optimization Algorithms and Entropy Measures for Image Segmentation 18

A Study of Various Optimization Algorithms and Entropy Measu...

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18th INDIAcom;11th International Conference on Computing for Sustainable Global Development, INDIACom 2024

作者： Gonthina, Nagamani Prasad, L. V. Narasimha Institute of Aeronautical Engineering Dept. of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9789380544519

One of the most challenging jobs in image processing techniques is image segmentation. To identify the objects of interest in an image, we segment the image into different parts and extract the interesting objects. Previous studies have addressed various techniques for image segmentation, like clustering, thresholding, watershed, neural networks, etc. However, a segmentation technique alone cannot achieve better-segmented results. Much research is being carried out by combining the segmentation technique with either an optimization algorithm or entropy measures to enhance the efficiency of segmented results in the segmentation process. So, there is a need to study various segmentation techniques combined with optimization algorithms or entropy measures. Recent studies focused only on segmentation techniques, entropy measures, or optimization algorithms rather than on these combinations. This paper presents various works based on optimization techniques and entropy measures combined with clustering and thresholding techniques of image segmentation. From the study it is observed that most of the work is carried out in combining thresholding-based image segmentation techniques with optimization algorithms and entropy measures. © 2024 Bharati Vidyapeeth, New Delhi.

关键词： Entropy measures Image segmentation Optimization algorithms Segmentation techniques

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A METHOD TO ESTIMATE THE EARLY AGE COMPRESSIVE STRENGTH OF CONCRETE BEFORE DEMOLDING USING A PIN PENETRATION DEVICE

Journal of Structural and Construction Engineering

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Journal of Structural and Construction engineering 2023年第803期88卷 18-26页

作者： Nishiwaki, Tomoya Takasugi, Fumiya Narantogtokh, Bayarjavkhlan Hara, Shoumi Maliha, Maisha Dept. of Architecture and Building Science Graduate School of Engineering Tohoku University Japan Dept. of Architecture and Civil Engineering Faculty of Engineering Tohoku University Japan Dept. of Architecture Graduate School of Engineering The University of Tokyo Japan

This study proposed a method to estimate the compressive strength of early-age concrete before demolding using a pin penetration device. Small holes were prepared on molds to apply for this method. A strong correlation was obtained between pin penetration dept. and compressive strength despite the materials and mix proportions difference. This method was applicable between 5 and 15 MPa compressive strength, which is the demolding criterion. Within the experimental results, the penetration dept.s of 12.5, 9.5, and 8.5 mm (Flat), and 13.0, 10.0, and 9.0 mm (Sharp) were sufficient to demolding criteria of 5, 10, and 15 MPa, respectively. © 2023 Architectural Institute of Japan. All rights reserved.

关键词： Compressive strength

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Flood Prediction System with Voting Classifier 2

Flood Prediction System with Voting Classifier

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2nd IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2024

作者： Akshitha, K. Vijay Anand, M. Saveetha Engineering College Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350372847

Flood prediction plays a vital role in reducing the influence of natural disasters among the communities and the infrastructure. This paper presents a machine learning approach for flood prediction using a voting classifier, combining information from turbidity, gauge height, and discharge as key features. The dataset, comprising historical records of these parameters, serves as the foundation for training and evaluating the predictive models. In our work, we employ a voting classifier, integrating Random Forest and Gradient Boosting algorithms to harness the strengths of diverse models. The ensemble approach aims to enhance prediction accuracy and robustness by aggregating the predictions of individual classifiers. The choice of features turbidity, gauge height, and discharge is motivated by their relevance in capturing hydrological dynamics associated with flood events. The dataset is preprocessed to handle missing values, normalize features, and encode the target variable. The voting classifier is being trained upon a subset of data, and its accomplishment is estimated on a discrete test set. Various metrics, including accuracy, are employed to assess the model's predictive capabilities. Results indicate promising performance in flood prediction, showcasing the potential of ensemble methods for such applications. This system assures an accuracy of about approx. 99% and alert notification will be sent to the residents of flood predicted areas. Hence, this contributes to the ongoing efforts in leveraging machine learning techniques and also provides an alert notification for enhancing disaster preparedness and response with proper communication. © 2024 IEEE.

关键词： Turbidity

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Designing and Implementing an IoT-Based Long-Distance Fire-Extinguishment System 'Stationary 3-Axis Fire-Emergency Response Base' 8

Designing and Implementing an IoT-Based Long-Distance Fire-E...

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8th International Conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Kundu, Shyantan Dey, Swarajit Abdullha, Md. Mustafa Sen, Gobinda Gangopadhyay, Malay Institute of Engineering & Management Dept. of Computer Science & Engineering Kolkata India Institute of Engineering & Management Dept. of Electronics & Communication Engineering Kolkata India

ISBN: (纸本)9798331520762

This paper presents the design and implementation of an Internet of Thing (IOT)-based long-distance fire-extinguishment system named 'Stationary 3-Axis Fire-Emergency Response Base' (STAFERB). The system aims to provide immediate response to fire hazards in various environments, including residential areas, building complexes, commercial areas, small-scale industries, large factories, dense slum areas, and remotely located forest regions. The proposed STAFERB is designed to receive fire location coordinates, fire intensity, and probable cause information. It then calculates the path of a projectile based on considering obstacles and self-detection of a clear line of sight to reach the t1arget location. The system uses this information to select the appropriate speed and type of fire retardant. The designed STAFERB features robust communication, self-path detection, auto-orientation, and reduced response time, making it a significant advancement in fire-fighting technology. Experimental results demonstrate the effectiveness of the proposed STAFERB in mitigating fire hazards and hence can be well suited for real-time disaster management. © 2025 IEEE.

关键词： Fire hazards

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A Comprehensive Analysis on Skin Cancer Classification Using Transfer Learning 3

A Comprehensive Analysis on Skin Cancer Classification Using...

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3rd International Conference on Advancement in Electrical and Electronic engineering, ICAEEE 2024

作者： Renu, Israt Zerin Haque, Md. Momenul Paul, Subrata Kumer Mou, Mahmuda Shirin Rahman, Md Nahid Gupta, Sudipta Sen Paul, Rakhi Rani Rabindra Maitree University Dept. of Computer Science and Engineering Kushtia7000 Bangladesh Qadirabad Dept. of Computer Science and Engineering Dayarampur Natore6431 Bangladesh University of Rajshahi Dept. of Computer Science and Engineering Rajshahi6205 Bangladesh

ISBN: (纸本)9798350388282

Skin cancer, a prevalent and potentially life-threatening disease, necessitates accurate and timely diagnosis for effective treatment. In addressing this critical healthcare challenge, this research proposes a novel approach to skin cancer classification. The study identifies the need for enhanced diagnostic accuracy and introduces a solution by integrating the efficiency of transfer learning, specifically utilizing the EfficientNet V2 architecture. The proposed methodology leverages pre-trained convolutional neural networks (CNNs) to extract high-level features from dermatological images, which are subsequently input into a sophisticated classifier, forming a robust framework for precise skin cancer classification. The research employs a diverse dataset comprising 3297 images sourced from the publicly available Skin Cancer ISIC archive dataset, ensuring comprehensive coverage of benign and malignant skin lesions. During the training phase, the proposed EfficientNet V2 transfer learning approach outperforms conventional methods, achieving an impressive accuracy of 84%, surpassing both Inception V3 (82%) and a generic CNN model (81%). Interpretability analysis is conducted to elucidate the decision-making process of the EfficientNet V2 model, providing insights into the key features influencing its predictions. This transparency is crucial for building trust in the model's recommendations and facilitating its seamless integration into clinical workflows. The EfficientNet V2 transfer learning approach emerges as a promising and effective solution for advancing skin cancer classification systems, contributing significantly to the ongoing efforts to improve diagnostic accuracy in dermatology. © 2024 IEEE.

关键词： Decision making

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Breast Cancer Classification via the Use of ML and DL: A Comprehensive Review 3

Breast Cancer Classification via the Use of ML and DL: A Com...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Sarang, Ruvanshi Dabhi, Vipul Shah, Arpit Dept. of Computer Science Engineering Parul University Vadodara India Dept. of Computer Engineering Parul University Vadodara India

ISBN: (纸本)9798331532420

New and improved methods of diagnosis are needed because breast cancer is still the leading cancer-related killer worldwide. Updates to the methods used to categorize breast cancer have emerged because of recent advances in DL and ML. This review paper’s goal in conducting this research is to bring together existing breast cancer diagnostic and classification methods that make use of deep learning and machine learning techniques. We examine the many approaches, datasets, and performance indicators used in the industry and point out the pros and cons of each as part of the review paper. This review aims to thoroughly examine all the methods that are now in use, spot any trends, and propose new areas for research. This review helps researchers and practitioners understand the current state of breast cancer categorization by combining the findings of past research. Discuss recent advances in ML/DL techniques for improving classification, such as ensemble methods, transfer learning, or hybrid models. Additionally, propose the use of fine-tuning of hyperparameters or novel architectures to enhance classification. © 2024 IEEE.

关键词： Adversarial machine learning

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Effect of pulsed current cathodic protection on pipeline steel API 5L X65 corrosion mitigation: An investigation and machine learning-assisted modeling

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Corrosion Communications 2023年第4期12卷 29-45页

作者： Hosein Eslamian Mehdi Javidi Mohammad Reza Zamani Mohammad Mahdi Dana Eghbal Mansoori Department of Materials Science and Engineering School of Engineering Shiraz University School of Metallurgy and Materials Engineering College of Engineering University of Tehran Computer Science Eng.and IT Dept. School of Electrical and Computer Engineering Shiraz University

Pulsed current cathodic protection(PCCP) could be more effective than direct current cathodic protection(DCCP)for mitigating corrosion in buried structures in the oil and gas industries if appropriate pulsed parameters are chosen. The purpose of this research is to present the corrosion prevention mechanism of the PCCP technique by taking into account the effects of duty cycle as well as frequency, modeling the relationships between pulse parameters(frequency and duty cycle) and system outputs(corrosion rate, protective current and pipe-to-soil potential) and finally identifying the most effective protection conditions over a wide range of frequency(2–10 kHz) and duty cycle(25%-75%). For this, pipe-to-soil potential, pH, current and power consumption, corrosion rate, surface deposits and investigation of pitting corrosion were taken into account. To model the input-output relationship in the PCCP method, a data-driven machine learning approach was used by training an artificial neural network(ANN). The results revealed that the PCCP system could yield the best protection conditions at 10 kHz frequency and 50% duty cycle, resulting in the longest protection length with the lowest corrosion rate at a consumption current 0.3 time that of the DCCP method. In the frequency range of 6–10 kHz and duty cycles of 50%-75%, SEM images indicated a uniform distribution of calcite deposits and no pits on cathode surface.

关键词： Pulsed current cathodic protection Duty cycle Frequency Artificial neural network API X65 pipeline steel

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Cybersecurity Threat Detection Using Deep Learning and Anomaly Detection Techniques

Cybersecurity Threat Detection Using Deep Learning and Anoma...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Rajendran, T. Mohamed Imtiaz, N. Jagadeesh, K. Sampathkumar, Balaji Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai India Nagarjuna College of Engineering and Technology Dept. of Information Science and Engineering Bangalore India Sri Venkateswaraa College of Technology Dept. of Computer Science and Engineering Chennai India Akshaya College of Engineering and Technology Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350359688

Through the use of combined deep learning and anomaly detection approaches, this research investigates the area of cybersecurity threat detection. The study proves the framework's extraordinary success in recognizing as well as mitigating diverse cyber dangers, including insider threats and zero-day attacks, through practical testing and case studies. The framework establishes itself as a proactive cybersecurity solution adaptive to the changing threat landscape due to its high accuracy of 95%, low amount of false positives 5%, and quick reaction times. The recommendations cover user training, scalability, regulatory compliance, including cross-disciplinary collaboration. Behavioral analysis, AI explainability, IoT security, and countering quantum computing concerns should be the main areas of future research. These revelations help the cybersecurity industry become more resilient and well-prepared. In this research investigation, experiments on threat detection were conducted using deep learning algorithms, with a specific focus on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The findings of the study reveal that a detection accuracy of 93% was achieved by CNN, while a higher accuracy of 96% in threat detection was exhibited by RNN. © 2024 IEEE.

关键词： Zero-day attack

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