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the Symbiosis of AI and Human Agency in Digital Authorship: Redefining the Landscape of Contemporary Literature 1

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7th international conference on Intelligent computing and Optimization, ICO 2023

作者： Ramesh, Radhika Venkatesh, Mekhala Girish, G.P. Hyderabad India University Bengaluru India Hyderabad India

ISBN: (数字)9783031508875

ISBN: (纸本)9783031508868

the rise of digital technology has dramatically changed the landscape of literature, affecting how readers interact with texts. Digital literary forms, such as e-books, interactive platforms, and audiobooks, have revolutionized the traditional modes of reading and writing. this shift has also influenced established literary customs, particularly the notion of the ‘Death of the Author’ as defined by Roland Barthes, transferring authority from creators to readers. Our research investigates the evolving role of authorship in the digital age, focusing on the collaboration between artificial intelligence (AI) and humans in storytelling. We explore the implications of AI’s influence on literature and envision a future where AI and human creativity coalesce to enrich literary expression and interpretation. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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Object Detection Based Automated Optical Inspection of Printed Circuit Board Assembly Using Deep learning 7th

Object Detection Based Automated Optical Inspection of Print...

引用

7th international conference on soft computing in Data Science (SCDS)

作者： Chiun, Ong Yee Ruhaiyem, Nur Intan Raihana Univ Sains Malaysia Sch Comp Sci Gelugor 11800 Penang Malaysia

ISBN: (纸本)9789819904044;9789819904051

Advancement of technologies in the electronics industry has render decrease in electronic components sizes and increase in number of components on Printed Circuit Board (PCB). Industries specialize in manufacturing Printed Circuit Board Assembly (PCBA), also implementing manual visual inspection in In Process Quality Control (IPQC) verification process to ensure quality of products. Such technology advancement has increased workload of operators and time taken to perform inspection. this study is aimed to reduce time consumption and cognitive load of operators, while ensuring consistency of visual inspection during component verification process by utilizing deep learning models to perform object detection based automated optical inspection of images consisting electronic components. three deep learning algorithms were used in the study, which are Faster R-CNN, YOLO v3 and SSD FPN. Both Faster R-CNN and SSD FPN utilized ResNet-50 backbone, whereas YOLO v3 was built with Darknet-53 backbone. Various input image dimension and image resizing options were explored to determine the best model for object detection. At the end of the study, SSD FPN with input image dimension resized to 640 x 640 by keeping image aspect ratio and with padding is concluded as the best localization and classification model to perform object detection for various types of components present in digital image.

关键词： Object detection Printed circuit board Printed circuit board assembly Deep learning

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Enhancement of Accuracy Measure by Detecting a Clinical Notes Using Support Vector machine 7th

Enhancement of Accuracy Measure by Detecting a Clinical Note...

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7th international conference on Intelligent computing and Optimization, ICO 2023

作者： Balaji, Ch Yaswanth, P.V. Koushik, T.N. Yashika Prabha, K. Ramachandran, M. KL Business School Koneru Lakshmaiah Education Foundation Andhra Pradesh Guntur522302 India REST Labs Krishnagiri Tamil Nadu Kaveripattinam India

ISBN: (纸本)9783031733239

this study evaluates the effectiveness of the Support Vector machine (SVM) algorithm over the Naive Bayes algorithm in classifying clinical notes by medical specialty. We introduced a novel Support Vector Classifier to perform accuracy and loss assessments using the DATA dataset from the Kaggle repository, consisting of 20 samples. these samples were analyzed using both algorithms. the SVM achieved a notable accuracy of 86.58% with a loss of 13.61%, outshining the Naive Bayes’ accuracy of 73.25% and loss of 26.74%. the significant superiority of SVM in classifying medical specialties was statistically validated through a two-tailed independent sample T-Test, yielding a p-value of 0.001 at a 95% confidence level. this study underscores the SVM's enhanced proficiency in the categorization of medical specialties from clinical notes in comparison to Naive Bayes. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Adversarial machine learning

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Smart Home and machine learning as a Sustainable Healthcare Solution: Review and Perspectives 1

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7th international conference on Smart City Applications, SCA 2022

作者： Lamiae, Eloutouate Hicham, Gibet Tani Fatiha, Elouaai Mohammed, Bouhorma FSTT Abdelmalek Essaadi University Tetouan Morocco FPL Abdelmalek Essaadi University Tetouan Morocco

ISBN: (数字)9783031268526

ISBN: (纸本)9783031268519

Over the past two years, the world has witnessed one of the worst pandemics due to the outbreak of coronavirus (covid19), which has infected hundreds of millions and claimed the lives of millions across the globe. If we have learned anything from this pandemic, it is that the actual healthcare systems are unreliable under situations of enormous pressure. Accordingly, the present investigation tackles smart healthcare paradigm as a solution to transform the classical healthcare model into a sustainable one. therefore, this paper reviews the most advances on remote healthcare monitoring technologies and introduces a novel smart home architecture combined with cloud computing and machine learning to create a sustainable solution for healthcare. Furthermore, a case study of a patient with heart disease is suggested to highlight the importance of using machine learning to automate medical monitoring at home. Additionally, an investigation of human behavior using neural network transformers is suggested as a perspective of the research in hand to examine patients’ activities at home using surveillance camera thus constructing a resilient remote healthcare model. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cloud computing

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Detection of Phishing Websites Using machine learning 5th

Detection of Phishing Websites Using Machine Learning

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5th international conference on soft computing and Signal Processing, ICSCSP 2022

作者： Kumar, Rahul Kumar, Ravi Sahu, Raja Kumar Patra, Rajkumar Ghosh, Anupam Department of CSE Netaji Subhash Engineering College Kolkata India

ISBN: (纸本)9789811986680

In the cyber-world, phishing attack is increasing rapidly and is a major matter of concern, which is resulting in losing large amount of money of Internet users every year. Our study on this field develops and creates a model which will predict whether a URL is phishing or not, i.e., it can predict whether a URL is phishing or legitimate. the goal of this study is to develop a web application software that detects phishing URLs from the collection of over 5000 phishing URLs and 5000 legitimate URLs, which are collected from open source and are fragmented into 80% training samples and 20% testing samples. the study provided a model for URL classification into phishing and legitimate URLs. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： machine learning

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Opportunities of Using machine learning Methods in Telecommunications and Industry 4.0 – A Survey 7th

Opportunities of Using Machine Learning Methods in Telecommu...

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7th EAI international conference on Management of Manufacturing Systems, MMS 2022

作者： Peraković, Dragan Periša, Marko Cvitić, Ivan Zorić, Petra Kuljanić, Tibor Mijo Aleksić, David University of Zagreb Faculty of Transport and Traffic Sciences Zagreb Croatia Hrvatska Elektroprivreda d.d Zagreb Croatia

ISBN: (纸本)9783031227189

Artificial intelligence can be considered a leading component of industrial transformation, but its application is also present in other areas, such as the telecommunications sector. Methodologies based on artificial intelligence, the most significant of which is machine learning, support these areas when predicting maintenance needs and reducing downtime. machine learning has many algorithms and methods of its own. this research presents machine learning methods and the possibilities of their use in the areas of telecommunications and Industry 4.0. With the improvement of new technologies such as higher internet speeds and 5G mobile networks in the field of telecommunications or Industry 4.0, comes the need for new and improved management and support for systems that use these new technologies. Some types of machine learning can be used for collecting data to improve user’s quality of service. Also, some types of them can be used to collect data on network traffic or, in general, for any system that needs to collect data, cluster data points, and analyze data. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Internet of things

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Expeditious Diagnosis of Cerebral Aneurysm on MR Brain Angiogram Images Utilizing soft computing Techniques 7

Expeditious Diagnosis of Cerebral Aneurysm on MR Brain Angio...

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7th international conference on Inventive Computation Technologies, ICICT 2024

作者： Anjaneya, L.H. Maheswari, K. Revathi, N. Siva, K.P. Soudagar, Manzoore Elahi M. Dhanraj, Joshuva Arockia Bapuji Institute of Engineering And Technology Department of Electrical And Electronics Engineering Karnataka Davangere577004 India Cmr Technical Campus Department of Computer Science And Engineering Hyderabad Telangana Kandlakoya501401 India Panimalar Engineering College Department of Electronics And Communication Engineering Tamil Nadu Chennai600123 India Sri Eshwar College of Engineering Department of Computer Science And Business Systems Tamil Nadu Coimbatore641202 India Saveetha Institute of Medical And Techinical Sciences Saveetha School of Engineering Department of Vlsi Microelectronics Tamil Nadu Chennai602105 India Department of Mechatronics Engineering Chennai Tamil Nadu Padur603103 India

ISBN: (纸本)9798350359299

It is always a challenging task for radiologists to detect Cerebral Aneurysms (CA). Unenhanced magnetic resonance angiography (MRA) has become a popular screening tool because of its non-invasiveness, lack of contrast, and ionizing radiation. Because of its screening role, MRA requires a high recognition rate. this paper presents machine learning (ML) methods for detecting CA in MRA data. MRA scans of normal and aneurysms are collected and evaluated. this research study is divided into two sections: segmentation and classification. First, K-Means Clustering (KMC) is used for aneurysm segmentation from MR images. Second, image classification is done. For that, the appropriate features are retrieved using feature extraction and selection. then Gray Level Run Length Matrix (GLRLM) is used for feature extraction and the Genetic Algorithm (GA) for feature selection. the GLRLM and GA features are provided independently to the ML models for training and testing. the ML models used in the research work are Artificial Neural Networks (ANN) and Modified Fuzzy C-Means (MFCM). the metrics are employed to evaluate the GLRM and GA features with ANN and MFCM. the combination of GA features with ANN produces the best results when compared to other combinations. the GA with ANN obtains an outstanding 98.33% accuracy, 100% specificity and precision, 96.67% sensitivity, and 98.31% F1 score. © 2024 IEEE.

关键词： Genetic algorithms

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Secure Localization Techniques in Wireless Sensor Networks Against Routing Attacks Using machine learning Models 5th

Secure Localization Techniques in Wireless Sensor Networks A...

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5th international conference on soft computing and Signal Processing, ICSCSP 2022

作者： Gebremariam, Gebrekiros Gebreyesus Panda, J. Indu, S. Department of Electronics and Communication Engineering Delhi Technological University Shahbad Daulatpur Main Bwana Road Delhi110042 India

ISBN: (纸本)9789811986680

Security is an essential hot research topic for detecting and localization of malicious nodes and improving the quality of service in wireless sensor networks (WSNs). WSNs are exposed to different routing attacks, including wormhole attacks, Sybil attacks, and blackhole attacks that manipulate the network resources and routing protocols, affecting the localization accuracy and quality of service in WSNs. this work proposed secure localization and detection of routing attacks in WSNs using hybrid optimized machine learning techniques with an average detection accuracy of 100% using binary classification with the application of cluster labeling K-means clustering technique. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Quality of service

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An Improved Teaching-learning-Based Optimization with Adaptive Moment Estimation for Global Optimization 7

An Improved Teaching-Learning-Based Optimization with Adapti...

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7th international conference on Algorithms, computing and Systems, ICACS 2023

作者： Wu, Xiaokui Zhou, Zhurong Wu, Duowan College of Computer & Information Science Southwest University Chongqing China Department of Information Service Industry Guangzhou Industry & Trade Technician College Guangzhou China

ISBN: (纸本)9798400709098

the Teaching and learning Optimization Algorithm (TLBO) simulates the two main stages of traditional classroom teaching. It is divided into the "teaching stage" and the "learning stage". However, the traditional teaching process is a relatively complex process, and the simulation process of this algorithm is relatively simple. therefore, this study introduces two new educational concepts and educational methods, "group teaching" and "adaptive learning", into the simulation process of traditional classroom teaching, and proposed an improved teaching-based optimization algorithm (TLBO-Adam) based on adaptive moment estimation. this study introduces group teaching in the "teaching stage" and uses Fuzzy C-means clustering (FCM) algorithm in the field of machine learning, and introduces Adaptive learning in the "learning stage" and uses an adaptive moment estimation algorithm with the characteristics of a goal-oriented mechanism to solve the problem of premature convergence and slow convergence of the TLBO algorithm. Finally, through designing comparative experiments, experimental results show that the improved algorithm TLBO-Adam is effective in 9 test functions and can find the global optimal solution quickly and efficiently compared with other swarm intelligence algorithms. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Global optimization

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the Effect of Comorbidity on the Survival Rates of COVID-19 Using Quantum machine learning 7th

The Effect of Comorbidity on the Survival Rates of COVID-19 ...

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7th international conference on Data Management, Analytics and Innovation, ICDMAI 2023

作者： Shahapure, Arsheyee Banerjee, Anindita Deshmukh, Rehan Computer Science and Engineering Dr. Vishwanath Karad MIT-World Peace University Paud Road Pune411036 India Centre for Development of Advanced Computing Corporate Research and Development C-DAC Innovation Park Panchavati Pashan Pune411008 India School of Biosciences and Technology Dr. Vishwanath Karad MIT-World Peace University Paud Road Pune411036 India

ISBN: (纸本)9789819914135

We report the effect of comorbidity on the survival rates of COVID-19 using quantum machine learning. Recent understanding of the novel coronavirus aims to verify the target organ of the virus, which could lead shortly to significant advances in the diagnosis and treatment of infected patients. An overview of the impact of the SARS-CoV-2 virus based on many different parameters such as age, type of comorbidity, and gender has been studied. the data calculations are done manually by referring to parent articles and using machine learning and quantum machine learning algorithm. It is helpful to verify the target age group, gender at risk of infection, and survival rates of the person. the data used is classical data, and quantum algorithms were run on it. We found out that the accuracy has increased to the classical machine learning state vector machine results. We found that pulmonary diseases are the most harmful type of comorbidity when an individual gets infected with COVID-19. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Coronavirus

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