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NP4G: Network Programming for Generalization

NP4G: Network Programming for Generalization

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Intelligent systems Conference (IntelliSys)

作者： Hara, Shoichiro Watanabe, Yuji Nagoya City Univ Nagoya Aichi Japan

ISBN: (纸本)9783031477201;9783031477218

In recent years, the development of Artificial Intelligence systems using neural network has been remarkable. However, this method has low explainability and is illogical. To solve this, there is an automatic programming method based on inductive inference. However, this method has also the problem of low versatility. In this study, we propose NP4G: Network Programming for Generalization, which can automatically generate programs by inductive inference. Because the proposed method can realize "sequence", "selection", and "iteration" in programming and can satisfy the conditions of the structured program theorem, it is expected that NP4G is a method that automatically acquires any programs by inductive inference. As an example, we automatically construct a bitwise NOT operation program from several training data by generalization using NP4G. Although NP4G only randomly selects and connects nodes, by adjusting the number of nodes and the number of phase of "Phased Learning", we show the bitwise NOT operation programs are acquired in a comparatively short time and at a rate of about 7 in 10 running. The source code of NP4G is available on GitHub as a public repository.

关键词： Inductive Inference Automatic Programming Knowledge Acquisition Genetic Programming Genetic Network Programming

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Towards a Zero-Defect in Welding: An Exploration of Factors to Improve the Training Data for Image Classification

Towards a Zero-Defect in Welding: An Exploration of Factors ...

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Intelligent systems Conference

作者： Javanbakhtghahfarokhi, Negin Lopez, Angel J. Rodriguez-Echeverria, Jorge Gautama, Sidharta Univ Ghent Dept Ind Syst Engn & Product Design Ghent Belgium FlandersMake Ugent Corelab ISyE Ghent Belgium ESPOL Polytech Univ Fac Ingn Elect & Comp Escuela Super Politecn Litoral ESPOL Campus Gustavo Galindo Km 30-5 Via PerimetralPOB EC-090112 Guayaquil Ecuador

ISBN: (纸本)9783031663284;9783031663291

Embracing zero-defect manufacturing in the era of Industry 4.0 in the welding industry requires understanding and addressing challenges linked to the methodologies and conditions governing the generation of datasets. We comprehensively explore the dataset composition of welding experiments across various works in the literature, aiming to discover patterns, variations, and similarities that affect the performance of defect detection methods. Our exploration revealed that, while considerable effort is dedicated to creating precise defect detection models, more attention needs to be paid to the conditions for composing an efficient training dataset considering the constraints of the real-time weld defect detection implementation. Therefore, in this study, we investigated the effect of the variation in the number of trials and the frame rate on defect detection by performing several experiments. We performed experiments using the Tungsten Inert Gas (TIG) welding Aluminium 5083 public dataset. Our findings show that a high frame rate does not always enhance the model's performance for defect detection. Additionally, we attained 78% accuracy with 16 trials (eight non-defective, eight. defective) for defect detection with our dataset. To enhance the performance of our defect detection setup by 11%, we would need to double the number of trials, presenting challenges in generating defective trials within an industrial context. The findings contribute valuable insights for enhancing the effectiveness of real-time defect detection in welding processes.

关键词： Defect detection Arc welding TIG Convolutional neural networks (CNN) Image classification Frame rate Training data Weld pool Molten pool Weld seam

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Application of Artificial Intelligence for Efficient Tumor Volume Segmentation of Lung Cancers in the 18FDG PET-CT Modality 4th

Application of Artificial Intelligence for Efficient Tumor V...

引用

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

作者： Salhi, Lotfi Mohsni, Chaima Slim, Ihsen Tunis El Manar Univ Lab Biophys & Med Technol Tunis Tunisia Salah Azaeiz Inst Dept Nucl Med Tunis Tunisia

ISBN: (纸本)9783031686528;9783031686535

Broncho-pulmonary cancer (BPC) remains the primary cause of morbidity from malignant tumors in Tunisia. By being the first killer of all cancers, bronchial cancer bears witness to a disastrous and bleak prognosis that presents a major public health problem worldwide. The diagnosis of cancer is largely based on indicators reflecting the stage of the tumor. Various medical imaging modalities are indicated in the chest scan, which is an important diagnostic tool in 18F-FluoroDeoxy-Glucose Positron Emission Tomography with Computed Tomography (FDG PET-CT) imaging. A completely automated diagnostic system's key means of detection is the accurate segmentation of cancer lesions in PET-CT images. The main objective of this work is to develop and test a deep learning algorithm (U-Net) for the detection of lung tumors of all stages in the FDG-PET/CT modality. The aim of this research is to develop an algorithm for the segmentation of malignant lung tumors using a convolutional neural network called U-Net. To enhance its efficiency, the transfer-learning technique is applied. The proposed network is distinguished by its efficient segmentation approach, which utilizes lightweight filtering to reduce computation and pointwise convolution to build additional features. In U-Net, skip connections were established with the "Relu" activation function to better model convergence, connecting the encoder layers to the decoder layers and allowing for the concatenation of feature maps with different resolutions. Additionally, the proposed model was trained and tested on the dataset obtained from the nuclear medicine department of the Institute of Salah Azaiez (ISA). The results showed an F-measure of 73.55%, accuracy of 99.99%, precision of 83.62%, and specificity of 99.99%. It is worth noting that the presented approach outperformed existing networks that require multiple phases of training and testing.

关键词： CNN Deep Learning Medical Imaging FDG PET-CT Segmentation U-NET Lung Cancer Pulmonary Nodule

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Evolution of ChatGPT and Different Language Models: A Review 1

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8th Smart Trends in Computing and Communications (SmartCom)

作者： Priyanka Kumari, Ritika Bansal, Poonam Dev, Amita Indira Gandhi Delhi Tech Univ Women Delhi India GuruGobindSinghIndraprasthaUniversity USICT New Delhi India

ISBN: (数字)9789819713134

ISBN: (纸本)9789819713127;9789819713134

Technology is being upgraded every day. Today artificial intelligence, machine learning, and natural language processing have been playing a crucial role in development of technology. ChatGPT is one such example, which has shown exceptionally great performance and prominent results in AI. It has taken all the market. For every task people use ChatGPT for help and faster completion of tasks. In this study, we will examine the performance, advantages, drawbacks, past research comparison, improvements, and suggestions.

关键词： Artificial intelligence Natural language processing Machine learning ChatGPT Language model

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Towards a Methodological Approach for Intelligent IS/IT Project Portfolio Dashboard 4th

Towards a Methodological Approach for Intelligent IS/IT Proj...

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

作者： Zaidouni, Anass Idrissi, Mohammed Abdou Janati Bellabdaoui, Adil Mohammed V Univ Rabat ENSIAS Rabat Morocco

ISBN: (纸本)9783031686528;9783031686535

With the advent of digital transformation, organizations have become more aware of sustaining and valuing their Portfolio of Information System (IS) and Information Technology (IT) initiatives in pursuit of strategic objectives. Thus, the IS/IT Project Management Office (PMO) should be equipped with IS/IT Project Portfolio Dashboards, to monitor the up-to-date performance of the IS/IT Project Portfolio and consequently carry out appropriate actions. Nevertheless, this performance is often compromised by uncertainty. In this paper, a methodological approach for intelligent IS/IT Project Portfolio Dashboard is designed based on three Key Performance Indicators (KPIs) which are Strategic Portfolio Value (SPV), IS/IT Project Portfolio ranking, and Overall Portfolio Risk (OPR). The proposed methodological approach measures the two first KPIs taking into consideration uncertainty to model respectively variability of strategic objectives in SPV and subjectivity of IS/IT PMO experts in IS/IT Project Portfolio ranking. Besides strategic interpretability, as an IS/IT PMO experts' knowledge, is incorporated in OPR. An IS/IT PMO Project Portfolio case study demonstrates the new methodological approach applicability in which the SPV equals 0.68, the computed ranking emphasizes two highly-priority scored IS/IT projects while ensuring decision-making reliability via a Consistency Ratio (CR) lower than 0.1, and the predicted OPR is obtained with a satisfactory Root Mean Square Error (RMSE) accuracy of 0.108. The new methodological approach advances the current body of knowledge on IS/IT Project Portfolio performance measurement under uncertainty forms and enables IS/IT PMOs to support data-driven decision-making and consequently carry out managerial actions appropriately.

关键词： IS/IT Project Portfolio Dashboard Strategic Portfolio Value Project Portfolio Ranking Overall Portfolio Risk Uncertainty IS/IT PMO

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An Efficient Prediction of Obstructive Sleep Apnea Using Hybrid Convolutional Neural Network 4th

An Efficient Prediction of Obstructive Sleep Apnea Using Hyb...

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4th Congress on Intelligent systems (CIS)

作者： Rahman, N. Juber Nithya, P. PSG Coll Arts & Sci Comp Sci Coimbatore India PSG Coll Arts & Sci Networking & Mobile Applicat Coimbatore India

ISBN: (纸本)9789819990368;9789819990375

Obstructive sleep apnea (OSA) represents a severe sleep disorder, which exhibited during the interruption of the breathing while sleeping. It causes as a result of inadequate supply of oxygen to both the brain and the physical body. In this research work, Convolutional Neural Network (CNN) has been used for feature extraction and selection and the Artificial Neural Network (ANN) has been used to predict sleep apnea. A flattened layer, fully connected networks, and feature extraction layers make up the suggested CNN model. LSTM model is used for classification, composed of Softmax classification layer to identify disease classification and to compute its association with cardiovascular diseases. Experimental analysis of the proposed framework toward the prediction of obstructive sleep apnea classification has been done using the Physionet apnea dataset. This dataset aims to evaluate the effectiveness of the proposed representative framework against the conventional approaches to analyze in various deep learning architectures. The proposed framework achieves accuracy of 99% on optimal feature classification for disease prediction against machine-based existing classification approaches.

关键词： Sleep apnea Convolutional Neural networks Artificial Neural networks Electrocardiogram LSTM

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SDlM: Improved Medical Synthesis of MRI Images Through Diffusion Models 13th

SDlM: Improved Medical Synthesis of MRI Images Through Diffu...

引用

13th Computer Science Online Conference

作者： Abd, Sura Ahmed Ali, Khawla Hussein Univ Basrah Dept Comp Sci Coll Pure Sci Basrah Iraq

ISBN: (纸本)9783031702846;9783031702853

We introduce SDIM, a latent diffusion model for the synthesis of medical MRI images. In contrast to earlier iterations of Stable Diffusion, SDIM uses a bigger UNet backbone. Since SDIM uses amedical MRI image synthesis encoder, the rise in model parameters is mostly caused by additional focus frames and a bigger cross-attention context. We create various conditioning plans and use various aspect ratios to teach SDIM. Additionally, we present a refinement model that employs a post-hoc image-to-image method to enhance the visual integrity of samples produced by SDIM. We show that SDIM performs far better than earlier iterations of Stable Diffusion and produces outcomes on par with those of cutting-edge, black-box image generators. We make code and model weights available in an effort to support open research and transparency in large-scale model training and evaluation. The findings for value (MSE 0.4728, Fid 1.9061, and SSIM 0.9217) that we were promised were obtained.

关键词： Diffusion Model MRI Image Synthesis SDIM

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Contextual Anomaly Detection in Smart Homes Using Temporal Graph Based Distances 1

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16th International Conference on Ubiquitous Computing and Ambient Intelligence

作者： Bodaghi, Amirhosein Nugent, Chris Ulster Univ Sch Comp Belfast North Ireland

ISBN: (数字)9783031775710

ISBN: (纸本)9783031775703;9783031775710

Identifying contextual anomalies in smart home environments is significant as it enables detailed health and medical applications, especially when the symptoms occur gradually instead of abruptly. Current methods often lack contextual awareness, limiting their effectiveness in accurately identifying anomalies within the complex activity patterns typical of smart home data. Our research addresses this gap by developing a method using graph edit distances (GED) to represent daily activity patterns. We first generated a synthetic dataset using a Markov model to mimic real-world smart home activity data. This process involved calculating transition probabilities and fitting various statistical distributions to the activity durations. Building upon this dataset, our anomaly detection approach involves constructing time series from GEDs data with multiple lag days and extracting residuals from them to avoid seasonality patterns. To evaluate our approach, we added synthetic anomalies to residuals by adjusting their values based on percentage changes across various sample sizes. We used two detection methods: Isolation Forest (IF) and Mahalanobis Distance (MD). IF was the most sensitive, with recall improving as the sample size increased, reaching 93.6% accuracy for 600 sample size at the highest percentage change. Conversely, MD's effectiveness decreased with increasing sizes. In conclusion, this study presents an approach for detecting contextual anomalies in smart homes using temporal graphs, with IF achieving promising results for large anomaly sample sizes.

关键词： Contextual anomaly Smart home environments Graph edit distance Multivariate anomaly detection Synthetic data generation Health monitoring

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Walking State Estimation for Biped Robot Using Foot Contact Information 1

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18th International Conference on Intelligent Autonomous systems (IAS)

作者： Lee, Haeseong Kim, Myeong-Ju Sung, Eunho Park, Jaeheung Seoul Natl Univ Grad Sch Convergence Sci & Technol Dept Intelligence & Informat Seoul South Korea Adv Inst Convergence Technol AICT Suwon South Korea Seoul Natl Univ Dept Intelligence & Informat Grad Sch Convergence Sci & Technol ASRIRICS Seoul South Korea

ISBN: (数字)9783031448515

ISBN: (纸本)9783031448508;9783031448515

Contact provides useful information in many applications. For bipedal walking, CoP-ZMP criterion, which is computed using the contact wrench between the foot and ground, is one of the important criteria to measure the stability of the biped robot. However, the CoP-ZMP criterion requires an accurate measure of contact point to define a support polygon, which is a difficult problem when the robot walks on uneven terrain. In this respect, this paper proposes an estimation framework, which is regardless of the support polygon, to recognize a current walking state (WS) using the contact information. The framework consists of two indicators and one discriminator. The indicators use the Gaussian Mixture Model to describe sub-states for each input. The discriminator determines the current WS by referencing rules with the outputs from the indicators. Finally, the framework is demonstrated with simulations and real robot experiments using a full-size humanoid robot, TOCABI.

关键词： Biped walking Contact state estimation

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Competency Analysis with a Neutrosophic Multi-Criteria Decision Making Method

Competency Analysis with a Neutrosophic Multi-Criteria Decis...

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International Conference on Intelligent and Fuzzy systems (INFUS)

作者： Sarucan, Ahmet Baysal, Mehmet Emin Konukman, Bald Konya Tech Univ Ind Engn Dept Konya Turkiye

ISBN: (纸本)9783031671913;9783031671920

One of the most important factors in the success of any system is the monitoring and analysis of employee performance. There are many ways to conduct performance analysis. One of them is the proven method of assessing performance through 360-degree feedback. In this method, employees rate their supervisors, their peers, their managers, and themselves. For team leaders working in the regional office of a telecommunications company, a 360-degree performance assessment method was used. For competencies, one of the four main components of the performance system, 8 team leaders were interviewed. A competency score was calculated. To test the consistency of the calculated score, the Neutrosophic Analytical Hierarchy Process (N-AHP) method was proposed. In the first step of the model, a decision matrix was constructed using the data obtained from the survey method. In the second step, the relative importance weights of eight competency in the calculation of the competency score were determined. In the third step, these weights were used to determine the competency rankings of eight team leaders. This was done using the N-AHP method. The results of the two methods were compared. It was found that the Neutrosophic method used in the study produced results that were consistent with each other.

关键词： Competency Analysis Neutrosophic Analytical Hierarchy Process Neutrosophic Sets Telecommunication Company

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