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Recommender systems play a crucial role in modern life, including information retrieval, the pharmaceutical industry, retail, and entertainment. The entertainment sector, in particular, attracts significant attention and generates substantial profits. This work proposes a new method for predicting unknown user-movie ratings to enhance customer satisfaction. To achieve this, we utilize the MovieLens 100K dataset. Our approach introduces an attention-based autoencoder to create meaningful representations and the XGBoost method for rating predictions. The results demonstrate that our proposal outperforms most of the existing state-of-the-art methods. Availability: github.com/ComputationIASBS/RecommSys

Breast cancer stands as a prevalent cause of fatality among females on a global scale, with prompt detection playing a pivotal role in diminishing mortality rates. The utilization of ultrasound scans in the BUSI dataset for medical imagery pertaining to breast cancer has exhibited commendable segmentation outcomes through the application of UNet and UNet++ networks. Nevertheless, a notable drawback of these models resides in their inattention towards the temporal aspects embedded within the images. This research endeavors to enrich the UNet++ architecture by integrating LSTM layers and self-attention mechanisms to exploit temporal characteristics for segmentation purposes. Furthermore, the incorporation of a Multiscale Feature Extraction Module aims to grasp varied scale features within the UNet++. Through the amalgamation of our proposed methodology with data augmentation on the BUSI with GT dataset, an accuracy rate of 98.88%, specificity of 99.53%, precision of 95.34%, sensitivity of 91.20%, F1-score of 93.74, and Dice coefficient of 92.74% are achieved. These findings demonstrate competitiveness with cutting-edge techniques outlined in existing literature.

Skin cancer (SC) stands out as one of the most life-threatening forms of cancer, with its danger amplified if not diagnosed and treated promptly. Early intervention is critical, as it allows for more effective treatment approaches. In recent years, Deep Learning (DL) has emerged as a powerful tool in the early detection and skin cancer diagnosis (SCD). Although the DL seems promising for the diagnosis of skin cancer, still ample scope exists for improving model efficiency and accuracy. This paper proposes a novel approach to skin cancer detection, utilizing optimization techniques in conjunction with pre-trained networks and wavelet transformations. First, normalized images will undergo pre-trained networks such as Densenet-121, Inception, Xception, and MobileNet to extract hierarchical features from input images. After feature extraction, the feature maps are passed through a Discrete Wavelet Transform (DWT) layer to capture low and high-frequency components. Then the self-attention module is integrated to learn global dependencies between features and focus on the most relevant parts of the feature maps. The number of neurons and optimization of the weight vectors are performed using three new swarm-based optimization techniques, such as Modified Gorilla Troops Optimizer (MGTO), Improved Gray Wolf Optimization (IGWO), and Fox optimization algorithm. Evaluation results demonstrate that optimizing weight vectors using optimization algorithms can enhance diagnostic accuracy and make it a highly effective approach for SCD. The proposed method demonstrates substantial improvements in accuracy, achieving top rates of 98.11% with the MobileNet + Wavelet + FOX and DenseNet + Wavelet + Fox combination on the ISIC-2016 dataset and 97.95% with the Inception + Wavelet + MGTO combination on the ISIC-2017 dataset, which improves accuracy by at least 1% compared to other methods.

Opposition-based learning (OBL) is an effective approach to improve the performance of metaheuristic optimization algorithms, which are commonly used for solving complex engineering problems. This chapter provides a comprehensive review of the literature on the use of opposition strategies in metaheuristic optimization algorithms, discussing the benefits and limitations of this approach. An overview of the opposition strategy concept, its various implementations, and its impact on the performance of metaheuristic algorithms are presented. Furthermore, case studies on the application of opposition strategies in engineering problems are provided, including the optimum locating of control systems in tall building. A shear frame with Magnetorheological (MR) fluid damper is considered as a case study. The results demonstrate that the incorporation of opposition strategies in metaheuristic algorithms significantly enhances the quality and speed of the optimization process. This chapter aims to provide a clear understanding of the opposition strategy in metaheuristic optimization algorithms and its engineering applications, with the ultimate goal of facilitating its adoption in real-world engineering problems.

Metaheuristics are stochastic optimization algorithms that mimic natural processes to find optimal solutions to complex problems. The success of metaheuristics largely depends on the ability to effectively explore and exploit the search space. Memory mechanisms have been introduced in several popular metaheuristic algorithms to enhance their performance. This chapter explores the significance of memory in metaheuristic algorithms and provides insights from well-known algorithms. The chapter begins by introducing the concept of memory, and its role in metaheuristic algorithms. The key factors influencing the effectiveness of memory mechanisms are discussed, such as the size of the memory, the information stored in memory, and the rate of information decay. A comprehensive analysis of how memory mechanisms are incorporated into popular metaheuristic algorithms is presented, and concludes by highlighting the importance of memory in metaheuristic performance and providing future research directions for improving memory mechanisms. The key takeaways are that memory mechanisms can significantly enhance the performance of metaheuristics by enabling them to explore and exploit the search space effectively and efficiently, and that the choice of memory mechanism should be tailored to the problem domain and the characteristics of the search space.

An improved bilinear fuzzy genetic algorithm (BFGA) is introduced in this chapter for the design optimization of steel structures with semi-rigid connections. Semi-rigid connections provide a compromise between the stiffness of fully rigid connections and the flexibility of fully pinned connections. However, designing such structures is challenging due to the non-linear behavior of semi-rigid connections. The BFGA is a robust optimization method that combines the strengths of fuzzy logic and genetic algorithm to handle the complexity and uncertainties of structural design problems. The BFGA, compared to standard GA, demonstrated to generate highquality solutions in a reasonable time. The application of the BFGA is demonstrated through the optimization of steel structures with semi-rigid connections, considering the weight, and performance criteria. The results show that the proposed BFGA is capable of finding optimal designs that satisfy all the design requirements and constraints. The proposed approach provides a promising solution for the optimization of complex structures with non-linear behavior.

Understanding land cover holds considerable potential for a myriad of practical applications, particularly as data accessibility transitions from being exclusive to governmental and commercial entities to now including the broader research community. Nevertheless, although the data is accessible to any community member interested in exploration, there exists a formidable learning curve and no standardized process for accessing, pre-processing, and leveraging the data for subsequent tasks. In this study, we democratize this data by presenting a flexible and efficient end to end pipeline for working with the Dynamic World dataset, a cutting-edge near-real-time land use/land cover (LULC) dataset. This includes a pre-processing and representation framework which tackles noise removal, efficient extraction of large amounts of data, and re-representation of LULC data in a format well suited for several downstream tasks. To demonstrate the power of our pipeline, we use it to extract data for an urbanization prediction problem and build a suite of machine learning models with excellent performance. This task is easily generalizable to the prediction of any type of land cover and our pipeline is also compatible with a series of other downstream tasks.

The Closest String Problem is an NP-hard problem that aims to find a string that has the minimum distance from all sequences that belong to the given set of strings. Its applications can be found in coding theory, computational biology, and designing degenerated primers, among others. There are efficient exact algorithms that have reached high-quality solutions for binary sequences. However, there is still room for improvement concerning the quality of solutions over DNA and protein sequences. In this paper, we introduce a three-stage algorithm that comprises the following process: first, we apply a novel alphabet pruning method to reduce the search space for effectively finding promising search regions. Second, a variant of beam search to find a heuristic solution is employed. This method utilizes a newly developed guiding function based on an expected distance heuristic score of partial solutions. Last, we introduce a local search to improve the quality of the solution obtained from the beam search. Furthermore, due to the lack of real-world benchmarks, two real-world datasets are introduced to verify the robustness of the method. The extensive experimental results show that the proposed method outperforms the previous approaches from the literature.

Current imaging methods for diagnosing BC are associated with limited sensitivity and specificity and modest positive predictive power. The recent progress in image analysis using artificial intelligence (AI) has created great promise to improve breast cancer (BC) diagnosis and subtype differentiation. In this case, novel quantitative computational methods, such as radiomics, have been developed to improve the sensitivity and specificity of early BC diagnosis and classification. The potential of radiomics in improving the diagnostic efficacy of imaging studies has been shown in several studies. In this review article, we discuss the radiomics workflow and current hand-crafted radiomics methods in the diagnosis and classification of BC based on most recent studies on different imaging modalities, e.g. MRI, mammography, contrast-enhanced spectral mammography (CESM), ultrasound imaging, and digital breast tumosynthesis (DBT). We also discuss current challenges and potential strategies to improve the specificity and sensitivity of radiomics in breast cancer to help achieve a higher level of BC classification and diagnosis in the clinical setting. The growing field of AI incorporation with imaging information has opened a great opportunity to provide a higher level of care for BC patients.