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

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A New Model Based on CNN for the Detection and Classification of Some Tumors 4th

A New Model Based on CNN for the Detection and Classificatio...

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

作者： Addou, Mohammed Mermri, El Bekkaye Gabli, Mohammed Mohammed Premier Univ Fac Sci ACSA Lab Oujda Morocco Mohammed Premier Univ Fac Sci LaRILab Oujda Morocco

ISBN: (纸本)9783031686597;9783031686603

This work introduces a new model based on convolutional neural networks (CNNs) for the detection and classification of five types of tumors: breast cancer, cervical cancer, lung and colon cancer, kidney cancer, and lymphoma. The proposed methodwas evaluated using three independent datasets corresponding to the first three types of tumors. Comparative analysis against three existing models from the literature revealed comparable performance, with accuracies of 98.80%, 97.47%, and 93.68%, respectively. Our model was applied to the remaining two diseases using two additional independent datasets, yielding promising results with accuracies of 99.87% and 95.33%, respectively.

关键词： Medical imaging CNN Classification

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CBR Evaluation Pyramid: A Pragmatic Process for Evaluating Case-Based Reasoning systems

CBR Evaluation Pyramid: A Pragmatic Process for Evaluating C...

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Intelligent systems Conference

作者： Jaiswal, Amar Rana, Shankar Norwegian Univ Sci & Technol Trondheim Norway Reva Univ Bengaluru Karnataka India

ISBN: (纸本)9783031664304;9783031664311

Case-based reasoning (CBR) systems can mimic human reasoning by referencing precedent cases. This paper proposes an integrated evaluation strategy for such systems by addressing the interdependence between the CBR process phases and the CBR knowledge containers that support them. The proposed evaluation process is termed as CBR evaluation pyramid. In real-world settings, this evaluation methodology has been instrumental in the development of a CBR system for a medical study, improving the CBR systems used by physiotherapists to treat musculoskeletal disorders. The proposed evaluation pyramid not only provides researchers with a comprehensive guide to develop CBR-specific evaluation strategies, but also offers practitioners a pragmatic tool for the ongoing evaluation and maintenance of CBR systems.

关键词： Case-based reasoning CBR evaluation CBR knowledge containers

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Localization of Region of Interest from Retinal Fundus Image for Early Detection of Glaucoma 9th

Localization of Region of Interest from Retinal Fundus Image...

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

作者： Jisy, N. K. Ali, Md Hasnat Senthil, Sirisha Srinivas, M. B. BITS Pilani Hyderabad Campus Hyderabad India LV Prasad Eye Inst Hyderabad India BITS Pilani Dubai Campus Dubai U Arab Emirates

ISBN: (纸本)9789819732982;9789819732999

Glaucoma is one of the serious ocular conditions that lead to permanent vision loss if not detected at the early stages of the disease. With the emergence of machine-learning techniques, computer-aided diagnosis is gaining prominence in the early detection of glaucoma. To an extent, this helps ophthalmologists in screening the eye to identify the progression of glaucoma. Convolutional neural networks (CNN) have gained remarkable significance in medical image analysis, and thus a deep CNN model has been explored in this paper to identify the changes brought in by glaucoma. A large number of glaucomatous and non-glaucomatous fundus images collected from a renowned eye institute have been used in this work. An appropriate region of interest is selected from these images as a part of pre-processing done using OpenCV and fed to the deep CNN architecture. The model is trained and tested with these pre-processed images, and the results obtained indicate that this technique has better accuracy and sensitivity.

关键词： Fundus images Glaucoma Pre-processing Deep convolutional neural network Deep learning

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Causal Analysis of Artificial Intelligence Adoption in Project Management 1

引用

Intelligent systems Conference (IntelliSys)

作者： Sarafanov, Egor Valilai, Omid Fatahi Wicaksono, Hendro Constructor Univ Sch Business Social & Decis Sci Campus Ring 1 D-28759 Bremen Germany

ISBN: (数字)9783031477218

ISBN: (纸本)9783031477201;9783031477218

Artificial intelligence (AI) technologies have great potential to improve decision-making and automation processes in various sectors, including project management. AI technologies could significantly contribute to overcoming the complexity of project management through process automation, cognitive insight, and engagement. However, the adoption of AI technologies still faces many challenges due to technical, human resource-related, organizational, and legal issues. Our research identified the potential factors that lead to the willingness of people and organizations to adopt AI technologies in project management. This paper proposes a causal model describing multivariate causal relationships between the driving factors and the willingness to adopt AI. The causal model is a set of hypotheses evaluated through a survey and causal analysis using the structural equation modeling (SEM) technique. The analysis focused on six factors influencing the willingness to adopt AI in project management, i.e., performance effectiveness, price, previous experience, feedback, complexity, and complementary technologies. Our research found that the perception of the high effectiveness of AI technologies leading to higher profits and overall the state of the project is the main factor influencing the willingness to adopt AI technologies in project management.

关键词： Artificial intelligence Project management Causal model Causal analysis Structural equation modeling

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Social Network Analysis: Beyond the Greediness in Community Detection Methods

Social Network Analysis: Beyond the Greediness in Community ...

引用

International Conference on Intelligent and Fuzzy systems (INFUS)

作者： Hernandez, Rodrigo Gutierrez, Inmaculada Castro, Javier Univ Complutense Madrid Madrid ES Spain

ISBN: (纸本)9783031671944;9783031671951

In the context of social network analysis, one of the main challenges concerns community detection. It has attracted the interest and significant attention towards contemporary multidisciplinary research in order to understand the complex structure of networks, unraveling clusters or communities within various interconnected systems. The Louvain algorithm has gained widespread popularity due to its remarkable calculation speed and efficiency, making it one of the most used methods. Although it is celebrated for its efficiency, its greedy nature poses certain challenges, especially in scenarios involving sparsely connected communities. Our study proposes an innovative method that enhances the performance of the Louvain algorithm without increasing its computational complexity. We explore the core applications of this method, seeking to refine and optimize the Louvain algorithm's performance. By addressing its weaknesses, particularly in handling low-connected communities, our research contributes to developing an improved version of this widely used algorithm. This enhancement is poised to broaden the algorithm's applicability and reliability, further cementing its status in the field of community detection within complex networks.

关键词： Social networks Analysis Complex networks Community Detection Problem Greedy Algorithm

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A Comparative Study on Two Apple Leaves Datasets Captured Under Diverse Conditions 9th

A Comparative Study on Two Apple Leaves Datasets Captured Un...

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

作者： Doutoum, Assad Souleyman Eryigit, Recep Tugrul, Bulent Ankara Univ Dept Comp Engn Ankara Turkiye

ISBN: (纸本)9789819732982;9789819732999

The importance of deep learning applications in our daily lives is growing in an increasingly complex world. Large amounts of data can be analyzed using deep learning, allowing for the identification of patterns, which can aid in solving problems in healthcare, finance, and agriculture. It can automatically extract the features of images captured for many different purposes. This study aims to detect apple leaf diseases using Vision Transformers from image datasets captured under contrasting conditions and compare their performance. Moreover, a brief overview of image processing and computer vision algorithms and applications is discussed. The results of our study indicate that deep learning models outperform traditional methods often used in image processing approaches in terms of accuracy. They also improve test accuracy compared with other studies in the same area of plant diseases. Furthermore, we conclude that the conditions under which images are acquired influence the performance of classification models. Thus, choosing the appropriate acquisition conditions requires careful consideration.

关键词： Deep learning Computer vision Image processing Vision transformers

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Exploring ChatGPT Prompt Engineering for Business Process Models Semantic Quality Improvement 12th

Exploring ChatGPT Prompt Engineering for Business Process Mo...

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

作者： Ayad, Sarah AlSayoud, Fatimah Arab Open Univ Fac Comp Studies Dammam Saudi Arabia

ISBN: (纸本)9783031602207;9783031602214

Business process modeling aims to enhance the comprehension of company processes, enabling decision-makers to attain strategic objectives. Nevertheless, inexperienced systems analysts who lack domain knowledge may produce low-quality models, thereby impacting the overall effectiveness of the modeling process. Large language models are bringing about a revolutionary transformation in the application of knowledge within various domains. This paper presents preliminary experimental findings on the potential application of Large language models in Business Process Modeling semantic quality improvement, specifically focusing on the extent to which these technologies can aid the modeler by suggesting improvements. In our study, we delve into the knowledge of GPT-4, a powerful language model. We aim to investigate its capabilities by employing various combinations of prompts, incorporating proposed textual syntax, and integrating contextual domain knowledge. Our objective is to leverage these approaches to improve the quality of business process models. Our findings indicate that the knowledge generated by GPT-4 is predominantly generic, encompassing ambiguous and general concepts that extend beyond the specific domain. However, when we apply our proposed prompting techniques, we observe a notable improvement in the specificity and comprehensiveness of the results. The utilization of these prompts helps to refine the generated knowledge, leading to more specific and comprehensive outcomes that align closely with the intended domain.

关键词： Business Process Modelling Semantic Quality Prompt Engineering

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Digital Technologies in the BiH Banking Industry - State and Perspective 10th

Digital Technologies in the BiH Banking Industry - State and...

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International Conference on New Technologies, Development and Application - Advanced Production Processes and Intelligent systems

作者： Abdic, Adem Rovcanin, Adnan Abdic, Ademir Kanlic, Fahir Univ Sarajevo Sch Econ & Business Trg Oslobodenja 1 Sarajevo 71000 Bosnia & Herceg Agcy Stat Bosnia & Herzegovina Ferhadija 11 Sarajevo 71000 Bosnia & Herceg

ISBN: (纸本)9783031662706;9783031662713

Financial and technological innovations over the last several decades in the banking industry have underpinned the emergence of new business models, strategies, processes, products, and applications. Furthermore, new entrants into the banking sector, providing financial services, have changed the dominant operating business models and competitive dynamics of the banking industry. The main purpose of this paper is to explore and analyze the state and perspectives of digital technologies application adoption in the banking sector, with a specific focus on bank risk management and supply services. In the paper, key drivers, challenges and benefits of digital technologies/innovations, market participants as well as key application areas in the banking industry are identified. The primary source of data in the research was collected through a structured questionnaire. The target population in the research consists of all banks that operated in the territory of BiH at the end of September 2023. The scientific contribution of this paper is reflected in the fact that it represents one of the pioneering analyses of the state and perspective of the use of digital technologies in BiH banks. Finally, for further research, it will be useful to consider and analyze how the continued growth and subsequent adoption of digital technologies impact consumers' satisfaction and overall perceptions of the technology.

关键词： Banking industry Digital technology Financial innovations Financial services Bosnia and Herzegovina

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Investigations on Deep Learning Pre-trained Model Inception-V3 Using Transfer Learning for Remote Sensing Image Classification on Benchmark Datasets 1

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4th Congress on Intelligent systems (CIS)

作者： Gupta, Nisha Singh, Satvir Singh, Jagtar Mittal, Ajay Joshi, Garima MRSPTU Bathinda India IKGPTU Jalandhar Punjab India Panjab Univ Chandigarh India Aryabhatta Grp Inst Barnala India

ISBN: (数字)9789819990405

ISBN: (纸本)9789819990399;9789819990405

Remote sensing image classification is the efficient execution of image categorization of high spatial resolution images for large remote sensing archives. High performance of image categorization is directly based on efficient image feature extraction. Before the widespread adoption of deep learning in remote sensing image classification, the feature extraction stage relied on manually designed low level features mainly focusing on basic features color, shape, and texture. However, these traditional handcrafted methods due to its lower performance were eventually replaced by convolutional neural networks which efficiently extracted abstract features. In the domain of remote sensing, significant results in image classification could be achieved through convolution neural networks combined with transfer learning. In this letter to enrich the accuracy of image classification using transfer learning pre-trained deep learning model Inception-V3 has been used as feature extractor for image classification on four benchmark datasets: UCMerced, AID, NWPU-RESISC45, and PatterNet dedicated for remote sensing scene classification. The proposed Inception-V3 combined with transfer learning produced improved accuracy of 87% on UCMerced, 75% on AID, 89% on PatterNet, and 90% on NWPU-RESISC45. The results demonstrate that the NWPU-RESISC45 benchmark dataset achieved the highest accuracy score of 90% surpassing UCMerced, AID, and PatterNet.

关键词： Remote sensing Image classification Deep learning

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Soil Type Identification via Deep Learning and Machine Learning Methods 5th

Soil Type Identification via Deep Learning and Machine Learn...

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

作者： Jalapur, Shruti Patil, Nagamma Natl Inst Technol Karnataka Dept Informat Technol Surathkal India

ISBN: (纸本)9789819720811;9789819720828

Soil type identification stands as a crucial concern in numerous countries, to ensure optimal crop yield, farmers need to accurately identify the suitable soil type for specific crops, which plays a significant role in meeting the heightened global food demand. The objective of this survey paper is to present a thorough and up-to-date overview of prevailing methodologies in soil identification, primarily focusing on image analysis, machine learning, and deep learning techniques. The paper initiates by highlighting the significance of soil identification and the limitations inherent in traditional methods. It then delves into the fundamental principles of image processing, deep learning, and spectroscopy, explaining how these techniques can be applied to soil identification. The survey presents an in-depth analysis of various image processing techniques employed for soil identification, including image segmentation, feature extraction, and classification algorithms. Furthermore, it discusses the application of deep learning models for soil classification based on image data.

关键词： Soil identification Image processing Deep learning Spectroscopy Classification Convolutional neural networks

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