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检索条件"丛书名=Lecture Notes in Networks and Systems"

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Particular Analysis of Regression Effect Sizes Applied on Big Data Set 1

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7th Computational Methods in systems and Software, CoMeSySo 2023

作者： Barot, Tomas Vaclavik, Marek Seberova, Alena Department of Mathematics with Didactics Faculty of Education University of Ostrava Fr. Sramka 3 Ostrava709 00 Czech Republic Department of Education and Adult Education Faculty of Education University of Ostrava Fr. Sramka 3 Ostrava709 00 Czech Republic Department of Preprimary and Primary Education Faculty of Education University of Ostrava Fr. Sramka 3 Ostrava709 00 Czech Republic

ISBN: (数字)9783031535529

ISBN: (纸本)9783031535512

In accordance with quantitative research, a wide spectrum of techniques can be seen. In the case of cardinal variables, regression analyses are suitable tools for the expression of dependences between observed variables. One of their options, the regression coefficients have been considered. However, the effect sizes analyses have not been so widely seen in research works in general. In this contribution, the big data analysis is being presented focusing on the regression effect size behavior following changing the number of samples. Two-dimensional and three-dimensional computations are applied with utilized mathematical regression models. The stable or stochastic behavior of Cohens f squared is discussed in the particular applied quantitative research of the OECD PISA with 397708 answers from respondents. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Logistic regression

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Modelling networks with Attached Storage Using Perfect Italian Domination 1

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1st International Conference on Machine Intelligence for Research and Innovations, MAiTRI 2023

作者： Poovathingal, Agnes Kureethara, Joseph Varghese Department of Mathematics Christ University Bangalore India

ISBN: (数字)9789819981298

ISBN: (纸本)9789819981281

Network-attached storage (NAS) is how data is stored and shared among hosts through a configured network. This is cheaper yet the best solution for sharing and using any huge unstructured data in an organization. Optimal distribution of NAS in a network of servers can be done using the concept of Perfect Italian Domination (PID). PID is a vertex labelling where the vertices of a graph G are labelled by 0, 1, 2 such that a vertex with label 0 should have a neighbourhood where the summation of the labels is exactly 2. The minimum possible sum of the labels obtained for graph G is its PID number. A network in an organization can have any structure. It can be highly interconnected, like a graph obtained from the Join of two graphs or the Corona product of two graphs. Hence, this paper discusses the PID of different graphs generated by the Join and the Corona products. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Graphic methods

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Transfer Learning Fine-Tuning Strategies for Enhanced Ultrasound Breast Cancer Classification 1

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International Conference on Advanced Intelligent systems for Sustainable Development, AI2SD 2023

作者： Bal-Ghaoui, Mohamed Alaoui, My Hachem El Yousfi Jilbab, Abdelilah Bourouhou, Abdennaser Laboratory National School of Arts and Crafts Mohammed V University in Rabat Rabat Morocco

ISBN: (数字)9783031523885

ISBN: (纸本)9783031523878

Artificial intelligence (AI) has emerged as a powerful tool in medical image analysis, revolutionizing the field of radiology and improving diagnostic accuracy and efficiency. Among various imaging modalities, ultrasound imaging is crucial in diagnosing a wide range of medical conditions due to its non-invasive nature and real-time imaging capabilities. However, the scarcity of labeled training data and the challenge of constructing effective learning frameworks pose significant hurdles in developing accurate and robust AI models for ultrasound image analysis. This research paper presents a study conducted on ultrasound images, specifically for breast cancer classification, and focuses on the application of Transfer Learning (TL) using state-of-the-art ImageNet pre-trained models including VGG16, VGG19, ResNet50, ResNet101, and InceptionV3. The study also explores the impact of different fine-tuning strategies on the final classification outcome. Strategies such as freezing 100% of layers, freezing 50% of layers, and scratch fine-tuning by training all layers were implemented along with the same common neural network-based classifier built on top. For reproducibility, publicly accessible datasets were used, namely Mendeley Breast and BUSI datasets. Additionally, a stratified 5-fold cross-validation technique was implemented to evaluate the pre-trained models, and metrics such as Accuracy, Sensitivity, Specificity, Precision, and False Positive Rate (FPR) were computed accordingly. This paper demonstrates the necessity of choosing the appropriate fine-tuning strategy aligned with the pre-trained model used. This can eventually enhance the feature extraction task, thus saving time and effort when implementing such an automatic classification framework for ultrasound images. In the application of breast ultrasound cancer classification, InceptionV3 has been found to be the consistent model across all strategies. Fine-tuning 50% of layers for this model has proved to ha

关键词： Diseases

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CenterNet: A Transfer Learning Approach for Human Presence Detection 1

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International Conference on Intelligent Manufacturing and Robotics, ICIMR 2023

作者： Cheng, Tang Jin Ab. Nasir, Ahmad Fakhri P. P. Abdul Majeed, Anwar Li, Lim Thai Mohd Khairuddin, Ismail Faculty of Manufacturing and Mechatronic Engineering Technology Universiti Malaysia Pahang Al-Sultan Abdullah Pahang Pekan26600 Malaysia Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah Pahang Pekan26600 Malaysia Xi’an Jiaotong-Liverpool University Suzhou215123 China TT Vision Holdings Berhad 11900 Plot 106 Sungai Hilir Keluang 5 Bayan Lepas FIZ.4 Pulau Pinang Bayan Lepas Malaysia

ISBN: (数字)9789819984985

ISBN: (纸本)9789819984978

Industrial robots have existed for decades, and their main design intention is to replace humans in those highly repetitive and dangerous tasks. Collaborative robots start to emerge in the industry as the market trends have moved towards the trend of high-mix low volume manufacturing. The concept of isolated working spaces is no longer ideal, and the acceptance issue of deploying collaborativerobots by humans arises due to safety concerns. Strategic use of the sensors and various approaches have attempted to address the issue. However, in the light of artificial intelligence technology, limited studies are found to leverage the transfer learning technology in improving the safety of collaborative robots. Hence, the present study aimed to incorporate the use of visual sensors and investigate the use of transfer learning approach—fine-tuning to help detect the presence of humans at first. As the initial steps, a custom image dataset that comprised of 1463 images was acquired from the surveillance system in TT Vision Holdings Berhad. Image annotation was done;accordingly with bounding boxes and data splitting of training, validation and test sets with 70:20:10 ratio was performed. More variation of the images was introduced by applying data augmentation techniques such as hue, brightness and contrast adjustment. COCO pre-trained CenterNet object detection model was used as the source model to perform the transfer learning techniques. Relevant metrics and tools were used to monitor the model learning during the fine-tuning process and evaluate the robustness of the fine-tuned models. As a result, the CenterNet-ResNet101_V1_FPN fine-tuned model has achieved 81.9% with 73.28 FPS and the CenterNet-ResNet50_V1_FPN fine-tuned model with 80.57% AP with 42.30% FPS. These findings evidently describe the usage of human presence detection, suggesting that the transfer learning approach can be considered as the method that is used to improve HRI safety. © The Author(s), under excl

关键词： Deep learning

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Hybrid Digital Educational systems as a Propaedeutics of Algorithms and Programming Courses at Universities 1

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7th Computational Methods in systems and Software, CoMeSySo 2023

作者： Leonov, Aleksandr G. Raiko, Mila V. Federal Research Center "Scientific Research Institute for System Analysis of the Russian Academy of Sciences" 36-1 Nakhimovsky Prosp. Moscow117218 Russia Moscow State University 1 Leninskiye Gory Main Building Moscow119991 Russia Moscow State Pedagogical University 1/1 Malaya Pirogovskaya Ulitsa Moscow119435 Russia State University of Management 9 Ryazansky Prospekt Moscow109542 Russia

ISBN: (数字)9783031548130

ISBN: (纸本)9783031548123

The using in the digital pedagogical educational environment of the already formed conceptual base of the student can greatly facilitate the study of the subject. Such an approach, for example, allows students to master the transition from direct management of the performer to program management. Executors of digital educational environments (PiktoMir, PiktoMir-K, KuMir) are a series of consecutively complicated objects from a general continuous course of algorithmization. Text-based digital pedagogic educational environments, such as KuMir, are used in the natural continuation of the course on the fundamentals of algorithmic learning for students. Robots with whom students learn in primary and propaedeutic courses can also be used in more complex language systems. Thus, program performers already well-developed by students, while preserving functionality and a set of commands, migrate from course to course, from pictographic digital educational environments to text-based digital pedagogical educational environments, such as KuMir, which allows the student to concentrate on mastering the new programming language and software system. This article discusses a methodology for smoothing out the complexities of the transition from pictographic (icon) programming to text-based programming. This approach makes it possible to build teaching programming not only in kindergarten or school, but also quite acceptable for quick learning in programming courses for beginners in universities. The authors describe many years of teaching experience at Moscow State Pedagogical University using digital educational environments KuMir, Pik-toMir, PiktoMir K. PiktoMir K made it possible to significantly facilitate the transition of students to mastering text-based programming languages. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Students

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Diagnosis of Faults in Electro-Mechanical Devices from Vibration Measurements 1

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12th World Conference on Intelligent systems for Industrial Automation, WCIS 2022

作者： Erdem, Binnur Demir Savaş, Mahmut Ahsen Dimililer, Kamil Mechanical Engineering Near East University Via Mersin 10 N. Cyprus Nicosia Turkey Electrical and Electronic Engineering Near East University Via Mersin 10 N. Cyprus Nicosia Turkey

ISBN: (数字)9783031515217

ISBN: (纸本)9783031515200

With new enhancements and developments, mechanical engineering got into a new stage of enhancement where the combination of artificial intelligence technology. We frequently see mechanical and electronic engineering's perception of artificial intelligence. This can utilize computers’ sophisticated data processing capabilities, solving a variety of complicated problems while also increasing the amount of automation in mechanical and electronic engineering. The main issue with traditional mechanical fault detection is that, because technical circumstances are so difficult and complex, only minor errors can be recognized by machines. As a result, the usage procedure is inconvenient. This research work combines some machine learning algorithms to analyze the rate of diagnosis of faults in Electro-Mechanical devices from vibration measurements. The main aim of this study is to employ ML techniques for the classification of mechanical failure analysis. The obtained dataset will be used to analyze the ML techniques. According to the results obtained, the Decision Tree algorithm has achieved the highest rate of 87.4%. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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Analysis of Spatiotemporal Variation of Oceanographic Parameters in the North Atlantic off Morocco 1

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International Conference on Advanced Intelligent systems for Sustainable Development, AI2SD 2023

作者： Falouss, Safae Harra, Abdessalam Ben Elallati, Samia Bouhadi, Amine Issa, Lamiae Khali Raissouni, Ahmed Arrim, Abdlekrim El Department of Geology EMRN Abdelmalek Essaadi University FSTT Tangier Morocco National Institute of Fisheries Research Casablanca Morocco

ISBN: (数字)9783031542886

ISBN: (纸本)9783031542879

Analysis of the spatiotemporal evolution of oceanographic parameters such as sea surface temperature (SST) and chlorophyll concentration (CHL) in the North Atlantic between 2018 and 2022 revealed interesting seasonal and interannual trends. Seasonal variations in SST and CHL are associated with factors such as sunlight, winds, and ocean currents. These two parameters showed an inverse relationship caused by several factors, of which the coastal upwelling is the most important. Interannual variations are influenced by weather conditions, extreme climatic events, and changes in ocean currents. The study of the spatiotemporal evolution of these two parameters is essential to understand environmental changes, assess the state of health of marine ecosystems and predict future changes. It also contributes to the establishment of appropriate management measures for the conservation of ocean ecosystems and to assess the impact of environmental disturbances. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Surface temperature

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A Review of Silk Farming Automation Using Artificial Intelligence, Machine Learning, and Cloud-Based Solutions 1

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2nd International Conference on Data Analytics and Learning, DAL 2022

作者： Raju, Chandrakala G. Sarkar, Somdyuti Canamedi, Varun Parameshwaranaik, J. Sarkar, Sukhabrata Department of Information Science and Engineering B.M.S. College of Engineering Bengaluru India Department of Medical Electronics Engineering B.M.S. College of Engineering Bengaluru India Central Sericulture Research and Training Institute Ministry of Textiles Government of India Berhampur India

ISBN: (数字)9789819963461

ISBN: (纸本)9789819963454

A comprehensive literature review on the applications of Artificial Intelligence (AI) and machine learning (ML), integrated with cloud computing solutions in sericulture, has been presented in the paper. The silk industry has a small percentage of the global textile market (less than 0.2%), but its production base is spread over 60 countries in the world. In India alone, it provides employment to more than 9 million people and adds revenue to the country's economy. However, this industry faces a few challenges in its various production stages such as egg counting, disease detection, cocoon deformity detection, and optimum environment control which directly influence the quality and quantity of raw silk production. The conventional approach to address these challenges incurred huge labor costs. Hence, the introduction of automation in this industry is an urgent need, and the implementation of AI techniques can prove to be a game changer since AI has already revolutionized various sectors of agriculture and provided better solutions by assisting agriculturists in the improvement of production as well as management. The present study covers various important contributions where AI, ML, and cloud computing solutions were employed to encounter the challenges related to the silk industry. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image segmentation

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Route Optimizations Using Genetic Algorithms for Wireless Body Area networks 1

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5th International Conference on Data and Information Sciences, ICDIS 2023

作者： Sharma, Gagan CSE Department Chandigarh University Mohali India

ISBN: (数字)9789819969067

ISBN: (纸本)9789819969050

There is a lot of research taking place in this fast and growing technology, namely Wireless Body Area networks (WBAN), to improve network performance. This work focuses to improve the WBAN system which is used in hospitals for the benefits of patients as well as doctors easiness. A lot of work can be identified in the same field and used as literature. Different researchers use different techniques to minimize energy consumption, packet delivery ration like Kruskal’s algorithm, Prim’s algorithm, particle swarm algorithm, etc. The energy of a sensor node gets impacted due to transmission distance, ideal routing protocols, and amount of data to be transmitted. Genetic algorithm (GA) which is a natural evolutionary algorithm and finds the optimal path to reach to destination which provides benefit to the patients in terms of their valuable data (patient’s report). Using GA along with fuzzy logic and cluster head selection, better results will be provided for packet delivery ratio and normalized residual energy, which further itself improves network lifetime. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Genetic algorithms

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Improving Breast Cancer Prognosis with DL-Based Image Classification 1

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5th International Conference on Computer and Communication Technologies, IC3T 2023

作者： Vinta, Surendra Reddy Dhanalaxmi, B. Aadilakshmi, S. Kumar, Pendyala Kamal Raju, Yeligeti School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati India Telangana Hyderabad India Telangana Hyderabad India

ISBN: (数字)9789819997077

ISBN: (纸本)9789819997060

Breast cancer is a serious global health problem, particularly among women, highlighting the crucial need for novel diagnostic and prognostic therapies. In this research, we delve into the potential of DL models, with a focus on ResNet50, VGG16, and AlexNet, to classify mammographic images, aiming to improve the prognosis of breast cancer. Leveraging the (DDSM) and its refined version, we engineered and evaluated models to proficiently distinguish mammographic images into normal, benign, and malignant classifications. According to our findings, all three deep learning architectures performed well on the test, with ResNet50 achieving the highest validation accuracy of 96.23%. The VGG16 and AlexNet models also performed well, with accuracies of 96.23% and 95.99%, respectively. These findings indicate that deep learning networks, specifically ResNet50, may efficiently identify mammography pictures, potentially boosting breast cancer prognosis accuracy. While our findings are encouraging, they also underscore the need for additional research. Future research should strive to refine these models further, possibly by using huge and more various datasets, and to investigate the utility of these models in a clinical setting. Furthermore, the development of interpretable models that can explain their conclusions could be a valuable path for future research. This would not only raise trust in these models, but it would also yield vital insights that could improve breast cancer prognosis even more. Finally, this study highlights the potential of DL models in improving breast cancer prognosis through correct mammography picture classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image classification

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