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Artificial Intelligence in Breast Cancer Diagnosis: "Synergy-Net" in Campania FESR-POR (European Fund of Regional Development-Regional Operative Program) Research Project 9th

Artificial Intelligence in Breast Cancer Diagnosis: "Synergy...

引用

9th International Congress on Information and Communication Technology (ICICT)

作者： Parmeggiani, Domenico Moccia, Giancarlo Luongo, Pasquale Miele, Francesco Allaria, Alfredo Torelli, Francesco Marrone, Stefano Gravina, Michela Sansone, Carlo Bollino, Ruggiero Ruggiero, Roberto Bassi, Paola Sciarra, Antonella Parisi, Simona Fisone, Francesca Donnarumma, Maddalena Claudia Colonnese, Chiara Della Monica, Paola Di Domenico, Marina Docimo, Ludovico Agresti, Massimo Univ Campania Luigi Vanvitelli Dept Adv Med & Surg Sci Naples Italy Univ Campania Luigi Vanvitelli Dept Woman Child & Gen & Specialized Surg Naples Italy Univ Federico II Dept Elect Engn & Informat Technol DIETI Naples Italy CINI ITEM Lab C Savy Via Cintia 21 Naples Italy Bollino It SpA Via Ustrie 31 Naples Italy Univ Campania Luigi Vanvitelli Div Gen Mini Invas & Obes Surg I-80131 Naples Italy Univ Campania Luigi Vanvitelli Dept Precis Med Naples Italy

ISBN: (纸本)9789819745807;9789819745814

Background: Nowadays, mammography and DCE-MRI are the gold standard for breast cancer screening. Scientific experience demonstrate an increasing application of Echo elastosonography in breast cancer diagnosis, for this reason, within the Synergy-Net project the objective is to create a system based on machine learning algorithms, a CAD developed with CNNs, capable of representing a decision support in the analysis of echo-elastic sonographic images. Results: 315 female patients at the "Vanvitelli" Breast Unit were subjected to digital 3D mammography tomosynthesis and advanced ultrasound (quantitative Echo elastosonography) and comparative analysis of the two methods. We collected 70 breast cancer, 200 benign pathologies (dropout of 11.1%) and we compared two different homogeneous by composition subgroups (35 with cancer and 100 with benign pathology 20.5%), relating to the diagnostic predictive capabilities of 3D digital mammography and quantitative elastosonography: Group A age 40-50 years, Group B age 50-60 years. Results demonstrate fair predictive performances of echoelastosonography versus traditional mammography, especially in the first group (40-50 years), p < 0.05 in favor of elastosonography regarding sensitivity and accuracy;performances that appear substantially comparable in the second group (50-60 years). We also tested the diagnostic predictive capabilities of the CAD Synergy-Breast-Net achieving encouraging results: Sensitivity 80%, Specificity 72%, Accuracy 74%, Negative Predictive Value 84.7% and Positive Predictive Value 83.3%. Conclusion: From this perspective, the screening of patients aged < 40 years and integrate the main screening activities in patients aged between 40 and 50 years integrated with increasingly high-performance machine learning systems, could represent a valid alternative in the future.

关键词： Breast cancer Elastography FibroScan CNNs Artificial intelligence

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Industry 4.0 Implementation in Moroccan Companies: Benefits and Barriers 4th

Industry 4.0 Implementation in Moroccan Companies: Benefits ...

引用

4th International Conference on Digital Technologies and Applications (ICDTA)

作者： Essalih, Safaa El Haouat, Zineb Ramadany, Mohamed Bennouna, Fatima Amegouz, Driss Sidi Mohammed Ben Abdellah Univ Higher Sch Technol Fes 30050 Morocco Sidi Mohammed Ben Abdellah Univ Fac Sci & Tech Fes 30050 Morocco Sidi Mohammed Ben Abdellah Univ Natl Sch Appl Sci Fes 30050 Morocco

ISBN: (纸本)9783031686498;9783031686504

Industry 4.0 incorporates a highly advanced concept of the factory of the future, based on the intelligent connection of machines and devices in a network that smoothly merges data and knowledge. To stay ahead in the Industry 4.0 era, companies must embrace digital and technological advances by integrating new technologies into their operations. Meanwhile, Moroccan companies are embarking on their industrial revolution. The goal of this work is to explore the benefits and barriers facing the implementation of Industry 4.0 in Moroccan companies and to achieve this, a structured questionnaire survey was conducted to gather relevant data. A total of 143 valid survey returns were captured and analyzed utilizing the Statistical Package for the Social Sciences (SPSS V25.0 for Windows).

关键词： industry 4.0 implementation survey Moroccan companies benefits barriers

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Method of Multifactor Analysis of the Alternatives Using the Example of Multi-criterial Selection of the Optimal Cargo Transportation Route 9th

Method of Multifactor Analysis of the Alternatives Using the...

引用

9th International Congress on Information and Communication Technology (ICICT)

作者： Konrad, Tetiana Pysarchuk, Oleksii Noga, Henryk Gajewska, Agnieszka Volkogon, Viktoriya Baran, Danylo Natl Aviat Univ 1 Liubomyra Huzara Ave UA-03058 Kyiv Ukraine Natl Tech Univ Ukraine Igor Sikorsky Kyiv Polytec 37 Prosp Peremohy UA-03056 Kyiv Ukraine Univ Natl Educ Commiss Krakow 2 Podchorazych St PL-30084 Krakow Poland

ISBN: (纸本)9789819732982;9789819732999

The article proposes a method of multifactorial analysis of alternatives to increase the reliability of determining the optimal route of cargo delivery on the graph of the transportation network. The basis of the proposed method is a multi-criteria mathematical model that formalizes the values of factors, indicators, and criteria for determining the optimal trajectory from the list of alternative routes. Alternative routes are described as possible options for traversing the graph of the transport network. The method allows you to make an unambiguous and reliable decision about the optimal transportation route.

关键词： Multi-criteria analysis Alternative Mathematical model Factor Automated traffic control systems

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Enabling Customer Segmentation Based on Classical Partitioning Methods Upon Statistical Evaluation for E-commerce Business Applications 1

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5th International Conference on Communication and Intelligent systems (ICCIS)

作者： Abdullah, A. Sheik Thalanki, Vaibhav Hariharan, Aakash Vellore Inst Technol Sch Comp Sci & Engn Chennai Tamil Nadu India

ISBN: (数字)9789819720538

ISBN: (纸本)9789819720521;9789819720538

In the present age, the prevailing spirit is centered around innovation, driving individuals to compete with one another. For businesses, the primary objective is not centered around acquiring new customers, but rather on how to maximize sales to their existing customer base. In the competitive field of e-commerce, it is essential to satisfy the customer's needs and identify the potential customer. This research project delves into advanced customer segmentation techniques using clustering algorithms, specifically K-means, K-medoids, Agglomerative Nesting (AGNES), and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). These clustering methods, coupled with meticulous hyperparameter tuning, enable us to uncover distinct customer groups within e-commerce businesses. By doing so, we gain invaluable insights into the unique characteristics, preferences, and behaviors of each customer segment. Through a comprehensive analysis of these segments, we aim to facilitate the development of highly targeted and personalized marketing strategies.

关键词： Customer analysis Clustering Unsupervised training K-means K-medoids AGNES DBSCAN

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Digital Innovations for Good: A Bibliometric Study of Social Entrepreneurship Digitalization 4th

Digital Innovations for Good: A Bibliometric Study of Social...

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4th International Conference on Digital Technologies and Applications (ICDTA)

作者： El Maaqili, Younes Ouchen, Abdessamad Sidi Mohamed Ben Abdellah Univ Fez Natl Sch Business & Management Fez LAREMEF Fes Morocco

ISBN: (纸本)9783031686740;9783031686757

As social enterprises seek out new tools to broaden the scope of their activities, digitisation is emerging as an important topic of interest to practitioners and researchers alike. Despite this interest, a gap persists in terms of bibliometric reviews concerning this research topic. This article aims to fill this gap by conducting a bibliometric review of the literature via semantic analysis, keyword-occurrences and co-authorship networks analysis in order to identify the research dynamics, the most cited articles and journals and the collaboration networks in the research field studied. The results show a remarkable increase in the number of articles published between 2011 and 2023, with the United States ranking first among the countries with the highest number of contributions. Thus, emerging markets case studies represents the most productive journal in terms of publications. Research trends include are digital platforms, e.g., crowdfunding;sustainable business models;and managerial resilience in times of crisis. This research will enable researchers and stakeholders in the social entrepreneurship ecosystem to better understand the dynamics of research on the subject of the digitalisation of social entrepreneurship. Future research should further study aspects of inclusive entrepreneurship;social impact assessment of digital initiatives and ethical considerations regarding the use of technological innovation, e.g., Artificial intelligence in social entrepreneurship.

关键词： Digitalization Bibliometric Mapping Social Entrepreneurship

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Anxiety and Depression Red Flag Marker in Social Media 1

引用

8th Smart Trends in Computing and Communications (SmartCom)

作者： Srivastava, Siddhant Das, Deepyaman Hassan, Md Kabirul Mondal, Raunak Singh, Anand Kumar Phani, Shanta Bengal Inst Technol Kolkata WB India

ISBN: (数字)9789819713233

ISBN: (纸本)9789819713226;9789819713233

The mental health issue is a big issue taken small in our society. In this post-COVID-19 era, this issue has taken a grave turn, where the detection, prevention, and avoidance measures need more serious handling. Social media forums such as Facebook have been observed to display mental health disorder symptoms. Social media can be used to detect anxiety and depression using machine learning models. In this paper, we have made use of social media data from a variety of different platforms to develop predictive models for the detection of depression or anxiety. We report the most common machine learning models as well as CNN and propose that identification of those suffering from anxiety and depression disorders may be achieved using prediction models to detect users' language on social media and has the potential to complement traditional screening. We also propose a browser extension that marks the danger zone the writer is in.

关键词： Anxiety Depression Social media Facebook CNN Machine learning

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Resume Classification Using LGBM Algorithm with Sentiment Analysis 8th

Resume Classification Using LGBM Algorithm with Sentiment An...

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8th Smart Trends in Computing and Communications (SmartCom)

作者： Angeline, R. Sowmiya, J. Malcom, Fredy Rachitha, K. SRM Inst Sci & Technol Dept Comp Sci & Engn Chennai India

ISBN: (纸本)9789819713196;9789819713202

The paragraph discusses the challenge of automating the classification of resumes, emphasizing the limitations of traditional manual methods due to the unstructured nature and diverse content found in resumes. To overcome these challenges, the article proposes the use of machine learning techniques for resume classification. Machine learning models can be trained on a large dataset of categorized resumes to identify patterns associated with different types of resumes, ultimately enabling faster, more precise, and impartial classification compared to manual methods. The proposed methodology combines sentiment analysis with the light gradient boosting machine (LGBM) algorithm to achieve this goal. The process involves several key steps, beginning with the collection of a dataset of resumes that have already been categorized into relevant job titles or categories. Next, the text within these resumes is preprocessed, which includes removing stop words and other irrelevant noise. Features are then extracted from the text, including its tone, keyword frequency, and length. The LGBM model is subsequently trained using this preprocessed resume dataset. The results of this approach are promising. The LGBM model exhibits an impressive 84% accuracy in categorizing resumes based on job titles, while sentiment analysis attains a perfect accuracy rate of 100%. Combining these two techniques results in a 1% increase in overall accuracy, demonstrating the effectiveness of this combined approach for resume classification.

关键词： Machine learning Resume classification Light gradient boosting algorithm Sentiment analysis

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Early Detection of Sepsis Utilizing Self-guided Generative Adversarial Network 9th

Early Detection of Sepsis Utilizing Self-guided Generative A...

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9th International Congress on Information and Communication Technology (ICICT)

作者： Hao, Pei-Yi Natl Kaohsiung Univ Sci & Technol Kaohsiung Taiwan

ISBN: (纸本)9789819735587;9789819735594

Sepsis is a severe illness that can potentially lead to death. According to statistics from the World Health Organization, approximately 6 million people die each year due to septic shock, with a high mortality rate of up to 50%. Early warning and timely intervention in the early stages of sepsis can prevent the occurrence of septic shock in the vast majority of cases. This paper presents a novel sepsis early warning system composed of a fully connected neural network, bidirectional long short-term memory, convolutional neural network, attention mechanism, cooperative network, and generative adversarial network. Among these components, the convolutional neural network can extract local features independent of position, bidirectional long short-term memory can capture time-dependent features of dynamic information using forward and backward operations, the fully connected neural network is suitable for analyzing key features within static information, and the attention mechanism can focus more on important features that enhance prediction performance. Additionally, the self-adjusting dual-channel module composed of collaborative networks and generative adversarial networks can generate random noise and enhance the robustness and generalization of features. In the experimental section, we validate our proposed method using the PhysioNet/Computing in Cardiology Challenge 2019 sepsis dataset, and the results confirm that our method outperforms other advanced sepsis prediction algorithms in terms of predictive performance.

关键词： Sepsis early warning model Deep learning Medical informatics Generative adversarial network

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Enhancing Industrial Automation and Control systems Cybersecurity Using Endpoint Detection and Response Tools 21st

Enhancing Industrial Automation and Control Systems Cybersec...

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21st International Conference on Smart Technologies and Education (STE) on Smart Technologies for a Sustainable Future

作者： Katulic, Filip Gros, Stjepan Sumina, Damir Erceg, Igor Univ Zagreb Fac Elect Engn & Comp Zagreb 10000 Croatia

ISBN: (纸本)9783031618901;9783031618918

In today's rapidly evolving cybersecurity landscape of industrial automation and control systems (IACSs), threat detection and prevention are more critical than ever. Compared to information technology (IT), the cybersecurity maturity of IACS is still low. Therefore, one way to increase the cybersecurity of IACS is to adopt and implement proven IT protection methods. Endpoint detection and response (EDR) and extended detection and response (XDR) tools have emerged as the means of protecting IT systems. Their exceptional threat detection, analysis, and response capabilities make them indispensable components for protecting critical infrastructures. The paper addresses the potential integration of EDR and XDR tools into IACS environments to strengthen their cybersecurity and protect them from new and existing threats. To demonstrate the feasibility of such a solution, we have implemented a chosen EDR tool on an industrial programmable logic controller (PLC). We have also analyzed the impact of such a solution on real-time PLC performance, which shows that such tools could be implemented in real-life IACSs.

关键词： Industrial automation and control systems (IACS) Operational technology (OT) Endpoint detection and response (EDR) Extended detection and response (XDR) Programmable logic controllers (PLC)

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Boats Imagery Classification Using Deep Learning 12th

Boats Imagery Classification Using Deep Learning

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12th World Conference on Information systems and Technologies (WorldCIST)

作者： Abrudan, Dumitru Dragulinescu, Ana-Maria Vizireanu, Nicolae Natl Univ Sci & Technol Politehn Bucharest Doctoral Sch Elect Telecommun & Informat Tech Bucharest 060042 Romania Natl Univ Sci & Technol Politehn Bucharest Telecommun Dept Bucharest 060042 Romania

ISBN: (纸本)9783031602207;9783031602214

Sea transportation is the cheapest way to ship goods all over the world. This type of transportation involves legal and illegal transportation. As an example of illegal activities, we can exemplify drug transportation, immigrants transportation etc. Compared to road transportation where all the routes are connected and monitored by cameras installed in different places, in sea transportation this cannot be possible. Other methods are used for ship monitoring such as satellites. Due to fact that satellite imagery requires lots of resources and high costs, alternative methods can be used. An alternative method in areas close to coastal sites are RGB cameras placed in specific zones suitable for anchoring. In this paper, we proposed to identify different type of images with boats taken in the water. To this goal, we trained a convolutional neural network (CNN) on a dataset comprising images with different types of boats such as bowrider, deck, dinghy, ponton, runabout, vela. Due to fact that at the time of image acquisition noise can be inserted in images, mathematical morphology is used to remove this noise. We obtained 98.27% accuracy using our proposed method for boat classification.

关键词： Mathematical Morphology Image Processing Deep Learning Maritime applications Surveillance

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