<abstract xmlns="http://www.w3.org/1999/xhtml">
Energy saving has always been a concern in production scheduling, especially in distributed hybrid flow shop scheduling problems. This study proposes a shuffled frog leaping algorithm with Q-learning (QSFLA) to solve distributed hybrid flow shop scheduling problems with energy-saving(DEHFSP) for minimizing the maximum completion time and total energy consumption simultaneously. The mathematical model is provided, and the lower bounds of two optimization objectives are given and proved. A Q-learning process is embedded in the memeplex search of QSFLA. The state of the population is calculated based on the lower bound. Sixteen search strategy combinations are designed according to the four kinds of global search and four kinds of neighborhood structure. One combination is selected to be used in the memeplex search according to the population state. An energy-saving operator is presented to reduce total energy consumption without increasing the processing time. One hundred forty instances with different scales are tested, and the computational results show that QSFLA is a very competitive algorithm for solving DEHFSP.
</abstract>

School of Mechanical Engineering, Anhui Polytechnic University

AnHui Key Laboratory of Detection Technology and Energy Saving Devices

A Shuffled Frog Leaping Algorithm with Q-Learning for Distributed Hybrid Flow Shop Scheduling Problem with Energy-Saving

<abstract xmlns="http://www.w3.org/1999/xhtml">
This research focuses on the development of an explainable artificial intelligence (Explainable AI or XAI) system aimed at the analysis of medical data. Medical imaging and related datasets present inherent complexities due to their high-dimensional nature and the intricate biological patterns they represent. These complexities necessitate sophisticated computational models to decode and interpret, often leading to the employment of deep neural networks. However, while these models have achieved remarkable accuracy, their ”black-box” nature raises legitimate concerns regarding their interpretability and reliability in the clinical context.
To address this challenge, we can consider the following approaches: traditional statistical methods, a singular complex neural network, or an ensemble of simpler neural networks. Traditional statistical methods, though transparent, often lack the nuanced sensitivity required for the intricate patterns within medical images. On the other hand, a singular complex neural network, while powerful, can sometimes be too generalized, making specific interpretations challenging. Hence, our proposed strategy employs a hybrid system, combining multiple neural networks with distinct architectures, each tailored to address specific facets of the medical data interpretation challenges.
The key components of this proposed technology include a module for anomaly detection within medical images, a module for categorizing detected anomalies into specific medical conditions and a module for generating user-friendly, clinically-relevant interpretations.
</abstract>

Department of Artificial Intelligence Systems

A Novel Explainable AI Model for Medical Data Analysis

<abstract xmlns="http://www.w3.org/1999/xhtml">
The National Synchrotron Radiation Center SOLARIS, ranked among the top infrastructures of that type worldwide, is the only one located in Central-Eastern Europe, in Poland. The SOLARIS Center, with six fully operational beamlines, serves as a hub for research across a diverse range of disciplines. This cutting-edge facility fosters innovation in fields like biology, chemistry, and physics as well as material engineering, nanotechnology, medicine, and pharmacology. With its advanced infrastructure and multidisciplinary approach, the SOLARIS Center enables discoveries and pushes the boundaries of knowledge. The most important aspect of such enormous research as well as industry infrastructure is to provide stable working conditions for the users and the conducted projects. Due to its unique properties, problem complexities, and challenges that require advanced approaches, the problem of anomaly detection and automatic analysis of signals for the beam stability assessment is still a huge challenge that has not been fully developed. To increase the effectiveness of centers with advanced research infrastructure we focus on the automatic analysis of transverse beam profiles generated by the Pinhole diagnostic beam-line. Pinhole beamlines are typically installed in the middle and high-energy synchrotrons to thoroughly analyze emitted X-rays and therefore assess electron beam quality. To address the problem we take advantage of AI solutions including up-to-date pre-trained convolutional neural network (CNN) models among others EfficientNetB0-B4-B6, InceptionV3 and DenseNet121. In this research, we propose the benchmark for Pinhole image classification including data preprocessing, model implementation, training process, hyperparameter selection as well as testing phase. Created from scratch database contains over one million transverse beam profile images. The proposed solution, based on the InceptionV3 architecture, classifies pinhole beamline images with 94.1% accuracy and 96.6% precision which is a state-of-the-art result in this research area. Finally, we employed interpretability algorithms to perform an analysis of the models and achieved results.
</abstract>

Department of Automatic Control and Robotics

AGH University in Krakow, Department of Automatic Control and Robotics

Automatic Analysis and Anomaly Detection System of Transverse Electron Beam Profile Based on Advanced and Interpretable Deep Learning Architectures

<abstract xmlns="http://www.w3.org/1999/xhtml">
Our previous experiments demonstrated that subsets of collections of (short) documents (with several hundred entries) share a common, normalized in some way, eigenvalue spectrum of combinatorial Laplacian. Based on this insight, we propose a method of incremental spectral clustering. The method consists of the following steps: (1) split the data into manageable subsets, (2) cluster each of the subsets, (3) merge clusters from different subsets based on the eigenvalue spectrum similarity to form clusters of the entire set. This method can be especially useful for clustering methods of complexity strongly increasing with the size of the data sample, like in case of typical spectral clustering. Experiments were performed showing that in fact the clustering and merging of subsets yield clusters close to clustering of the entire dataset. Our approach differs from other research streams in that we rely on the entire set (spectrum) of eigenvalues, whereas the other researchers concentrate on few eigenvectors related to lowest eigenvalues. Such eigenvectors are considered in the literature as of low reliability.
</abstract>

Eigenvalue-Based Incremental Spectral Clustering

<abstract xmlns="http://www.w3.org/1999/xhtml">
Recommender systems (RSs) often focus on learning users’ long-term preferences, while the sequential pattern of behavior is ignored. On the other hand, sequential RSs try to predict the next action by exploring relations between items in a user’s last interactions but do not consider the general preference. Recently, the performance of RSs has increased by unifying these two types of paradigms. However, existing methods still have two limitations. First, the user’s behavior uncertainty impedes precise learning of preferences. Second, being unable to understand the semantics of items makes the effect of the same item considered in the same way. These limitations jointly prevent RS from learning multifaceted preferences to capture the actual intentions of users. Existing methods have not properly addressed these problems since they ignore context-aware interactions between the user and item in terms of the links between the user and item attributes and sequential user actions over time. To address these challenges, this paper proposes a novel model, called the Dynamic Intention-Aware Recommendation with attention-based context-aware item attributes modeling (DIARec), which is capable of determining users’ preferences based on their goal intention, taking into account the influence of various item features on user decision-making in their current context. Specifically, to model users’ dynamic intentions, we introduce a dynamic intent-aware module to represent the hierarchical relations between items and their attributes in a given session. Experiments on benchmark datasets indicate that the proposed model DIARec outperforms other state-of-the-art methods.
</abstract>

Diarec: Dynamic Intention-Aware Recommendation with Attention-Based Context-Aware Item Attributes Modeling

AHEAD OF PRINT

Volume 14 (2024): Issue 2 (March 2024)

Volume 14 (2024): Issue 1 (January 2024)

Volume 13 (2023): Issue 4 (October 2023)

Volume 13 (2023): Issue 3 (June 2023)

Volume 13 (2023): Issue 2 (March 2023)

Volume 13 (2023): Issue 1 (January 2023)

Volume 12 (2022): Issue 4 (October 2022)

Volume 12 (2022): Issue 3 (July 2022)

Volume 12 (2022): Issue 2 (April 2022)

Volume 12 (2022): Issue 1 (January 2022)

Volume 11 (2021): Issue 4 (October 2021)

Volume 11 (2021): Issue 3 (July 2021)

Volume 11 (2021): Issue 2 (April 2021)

Volume 11 (2021): Issue 1 (January 2021)

Volume 10 (2020): Issue 4 (October 2020)

Volume 10 (2020): Issue 3 (July 2020)

Volume 10 (2020): Issue 2 (April 2020)

Volume 10 (2020): Issue 1 (January 2020)

Volume 9 (2019): Issue 4 (October 2019)

Volume 9 (2019): Issue 3 (July 2019)

Volume 9 (2019): Issue 2 (April 2019)

Volume 9 (2019): Issue 1 (January 2019)

Volume 8 (2018): Issue 4 (October 2018)

Volume 8 (2018): Issue 3 (July 2018)

Volume 8 (2018): Issue 2 (April 2018)

Volume 8 (2018): Issue 1 (January 2018)

Volume 7 (2017): Issue 4 (October 2017)

Volume 7 (2017): Issue 3 (July 2017)

Volume 7 (2017): Issue 2 (April 2017)

Volume 7 (2017): Issue 1 (January 2017)

Volume 6 (2016): Issue 4 (October 2016)

Volume 6 (2016): Issue 3 (July 2016)

Volume 6 (2016): Issue 2 (April 2016)

Volume 6 (2016): Issue 1 (January 2016)

Volume 5 (2015): Issue 4 (October 2015)

Volume 5 (2015): Issue 3 (July 2015)

Volume 5 (2015): Issue 2 (April 2015)

Volume 5 (2015): Issue 1 (January 2015)

Volume 4 (2014): Issue 4 (October 2014)

Volume 4 (2014): Issue 3 (July 2014)

Volume 4 (2014): Issue 2 (April 2014)

Volume 4 (2014): Issue 1 (January 2014)

Volume 3 (2013): Issue 4 (October 2013)

Volume 3 (2013): Issue 3 (July 2013)

Volume 3 (2013): Issue 2 (April 2013)

Volume 3 (2013): Issue 1 (January 2013)

Journal of Artificial Intelligence and Soft Computing Research

 Print version published by: The Journal of the Polish Neural Network Society and the University of Social Sciences in Lodz  Editor-in-Chief: Prof. Leszek Rutkowski, IEEE FellowFrequency of publishing: quarterlyMonths of publication: January, April, July, October  ISSN (print): 2083-2567ISSN (on-line): 2449-6499  Journal of Artificial Intelligence and Soft Computing Research is a dynamically developing international journal focused on the latest scientific results and methods constituting traditional artificial intelligence methods and soft computing techniques. Our goal is to bring together scientists representing both approaches and various research communities.  Why JAISCR?   Premier source of high quality research,  We have practice in publishing innovative articles dealing with various aspects of artificial intelligence methods and soft computing,  Extensive scope,  We are focused on permanent development of our journal.   JAISCR research areas  JAISCR publishes high quality, innovative research results in various areas of artificial intelligence and soft computing. These areas include, but are not limited to:   AI in Modelling and Simulation,  AI in Scheduling and Optimization,  Automated Reasoning and Inference,  Bioinformatics,  Cognitive Aspects of AI,  Computer Vision and Speech Understanding,  Data Mining,  Distributed Intelligent Processing,  Evolutionary Design,  Expert Systems,  Fuzzy Modelling and Control,  Hardware Implementations,  Heuristic Search,  Hybrid Models,  Information Retrieval,  Intelligent Database Systems,  Knowledge Engineering,  Mechatronics,  Multi-agent Systems,  Natural Language Processing,  Neural Network Theory and Architectures,  Neuro-informatics and Bio-inspired Models,  Pattern Recognition,  Robotics and Related Fields,  Rough Sets Theory: Foundations and Applications,  Supervised and Unsupervised Learning,  Swarm Intelligence and Systems,  Web Intelligence Applications &amp; Search,  Various Applications.   Submitting to JAISCR - a great advantage for the authorsWhy?   Fair and thorough peer review (at least three referees are required for the acceptance of each article),  Fast and efficient publication process,  Polishing the structure and technical format of each submission,  Open access (all articles published in JAISCR are open access - freely available on Sciendo platform),  Permanence (because of the open access form, permanent accessibility is assured),  High visibility within the field (the author's work is freely accessible to a global audience. In addition, articles are available through Sciendo),  Promotion of the published papers in the scientific community.   Archiving  Sciendo archives the contents of this journal in Portico - digital long-term preservation service of scholarly books, journals and collections.  Plagiarism Policy  The editorial board is participating in a growing community of Similarity Check System's users in order to ensure that the content published is original and trustworthy. Similarity Check is a medium that allows for comprehensive manuscripts screening, aimed to eliminate plagiarism and provide a high standard and quality peer-review process. 