Advancing malware imagery classification with explainable deep learning: A state-of-the-art approach using SHAP, LIME and Grad-CAM. by Sadia Nazim, Muhammad Mansoor Alam, Syed Safdar Rizvi, Jawahir Che Mustapha, Syed Shujaa Hussain, Mazliham Mohd Suud

“…There has been relatively little study on explainability, especially when dealing with malware imagery data, irrespective of the fact that DL/ML algorithms have revolutionized malware detection. Explainability techniques such as SHAP, LIME, and Grad-CAM approaches are employed to present a complete comprehension of feature significance and local or global predictive behavior of the model over various malware categories. …”
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Current Topics on the Integration of Artificial Intelligence in the Histopathological and Molecular Diagnosis of Uveal Melanoma by Serena Salzano, Giuseppe Broggi, Andrea Russo, Teresio Avitabile, Antonio Longo, Rosario Caltabiano, Manuel Mazzucchelli

“…AI contributes to more accurate tumor classification, assists in planning treatments, and improves the prediction of the prognostic indicators and molecular characteristics of the tumor. …”
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Optimization of Adaptive Observation Strategies for Multi-AUVs in Complex Marine Environments Using Deep Reinforcement Learning by Jingjing Zhang, Weidong Zhou, Xiong Deng, Shuo Yang, Chunwang Yang, Hongliang Yin

“…Additionally, experiments with three, five, and seven AUVs reveal that while increasing platform numbers enhances predictive accuracy, the benefits diminish with additional units.…”
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Air pressure forecasting for the Mutriku oscillating‐water‐column wave power plant: Review and case study by Jorge Marques Silva, Susana M. Vieira, Duarte Valério, João C. C. Henriques, Paul D. Sclavounos

“…A Least Squares Support Vector Machine (LS‐SVM) algorithm was developed to predict the air chamber pressure and compare it to the real signal. …”
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Artificial Intelligence in the Organization of Nursing Care: A Scoping Review by João Ventura-Silva, Maria Manuela Martins, Letícia de Lima Trindade, Ana da Conceição Alves Faria, Soraia Pereira, Samuel Spiegelberg Zuge, Olga Maria Pimenta Lopes Ribeiro

“…Conclusions: AI tools such as automated alert systems, predictive algorithms, and decision support transform nursing by increasing efficiency, accuracy, and patient-centered care, improving communication, reducing errors, and enabling earlier interventions with safer and more efficient quality care.…”
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Evaluation of Smart Building Integration into a Smart City by Applying Machine Learning Techniques by Mustafa Muthanna Najm Shahrabani, Rasa Apanaviciene

“…The existing literature on assessment methodologies reveals diverging evaluation frameworks for smart buildings and smart cities, non-uniform metrics and taxonomies that hinder scalability, and the low use of machine learning in predictive integration modelling. To fill these gaps, this paper introduces a novel machine learning model to predict smart building integration into smart city levels and assess their impact on smart city performance by leveraging data from 147 smart buildings in 13 regions. …”
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Dynamic Processes of Development Jointing of a Fractal Type: Models for a Solid-State Material of the Chamber in a Power Facility during its Operation by T. V. Ryzhova, D. N. Bukharov, M. M. Arakelyan, S. M. Arakelyan

“…The availability of a reliable database of their characteristics and operating modes of the working substance in real conditions with numerical parameters should allow, within the framework of the considered concepts, to fulfil predictive modeling and prediction of the durability of safe and stable operation of such devices and control their modes, taking into account appropriate metrological support.…”
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FBiLSTM-Attention short-term load forecasting based on fuzzy logic by Yan ZHANG, Zepeng KANG, Xiaozhi GAO, Nan YANG, Zhaolei WANG

“…In terms of model structure, a bi-directional long short-term memory (BiLSTM) and attention mechanism (Attention) were adopted. Finally, the prediction results of the proposed method with traditional LSTM and BiLSTM Attention models were compared. …”
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Optimizing Stroke Classification with Pre-Trained Deep Learning Models by Serra Aksoy, Pinar Demircioglu, Ismail Bogrekci

“…This study aims to investigate the use of deep learning techniques for predicting ischemic strokes with high accuracy, enabling earlier diagnosis and intervention. …”
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Research on Forecasting Methods of Agricultural Products Consumption Behavior Based on Unsupervised Learning by Hui Yang, Tian Yang, Xiangxin Ma, Jia Lin

“…After confirming the performance test of the algorithm, it verifies the prediction effect of this method on the consumption behavior of agricultural products. …”
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Estimation of the Time of Occurrence of the Maximum Electrical Demand by Selecting the Optimal Classification Model and Making Use of Unbalanced Data by César Aristóteles Yajure, Valesca M. Fuenzalida Sánchez

“…The most important conclusion drawn by the study is that the model obtained with the support vector machine algorithm turned out to be optimal, and successfully predicted the time of maximum demand on 15 of the 17 test days. …”
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OPERATIONAL ANALYSIS OF COMPLEX MEDICAL STATES BY PHOTONICS METHODS by A. I. Larkin, K. A. Trukhanov

“…Inverse two-phase coding and operational analysis of multi-parameter data can realize a number of probabilistic algorithms. The possibility and expediency of realization of not only the correlation algorithm underlying the holographic image recognition but also universal statistical algorithms using photonics methods is substantiated. …”
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Rapid Quality Assessment of Polygoni Multiflori Radix Based on Near-Infrared Spectroscopy by Bin Jia, Ziying Mai, Chaoqun Xiang, Qiwen Chen, Min Cheng, Longkai Zhang, Xue Xiao

“…The PLSR model for moisture, stilbene glycosides, and anthraquinone glycosides produced the calibration coefficient of determination (R2C) of 0.82, 0.52, and 0.58, the root mean square error of cross validation (RMSECV) of 0.91%, 0.77%, and 0.69%, the prediction coefficient of determination (R2P) of 0.72, 0.28, and 0.54, the root mean square error of prediction (RMSEP) of 0.65%, 0.81%, and 0.75%, and relative percentage differences (RPDs) of 1.7, 1.0, and 0.8. …”
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A statistical and machine learning approach for monthly precipitation forecasting in an Amazon city by Ewerton Cristhian Lima de Oliveira, Eduardo Costa de Carvalho, Edmir dos Santos Jesus, Rafael de Lima Rocha, Rafael de Lima Rocha, Helder Moreira Arruda, Ronnie Cley de Oliveira Alves, Ronnie Cley de Oliveira Alves, Renata Gonçalves Tedeschi

“…Besides the use of algorithms, another evaluation was conducted on Feature Composition based on statistical methods to investigate the impact of variables on the prediction.ResultsThe results obtained in our investigation indicate that the vector autoregressive moving average with exogenous regressors (VARMAX) model achieved the best performance in rainfall forecasting, with an average root mean square error (RMSE) of 9.1833 in time series cross-validation, outperforming the other models.DiscussionThe climate-driven patterns directly influenced the performance of the rainfall forecasting models evaluated in this study. …”
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Exploring the capabilities of hyperspectral remote sensing for soil texture evaluation by Mohammad Hosseinpour-Zarnaq, Mahmoud Omid, Fereydoon Sarmadian, Hassan Ghasemi-Mobtaker, Reza Alimardani, Pouya Bohlol

“…We explored various workflow combinations of environmental covariates, integrating hyperspectral data with topography (t), climate (c), soil characteristics (s), vegetation-land use (v), and land use to enhance predictions. The analysis clarified the significance of all factors influencing prediction performance. …”
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Forecasting Short- and Long-Term Wind Speed in Limpopo Province Using Machine Learning and Extreme Value Theory by Kgothatso Makubyane, Daniel Maposa

“…Future research could focus on enhancing prediction accuracy through hybrid algorithms and incorporating additional meteorological variables. …”
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AI-powered approaches for enhancing remote sensing-based water contamination detection in ecological systems by Li Yang, Zhang Ziwen, Xinhao Lin, Junmiao Hei, Yixiao Wang, Ang Zhang

“…Recent advancements in artificial intelligence (AI) offer promising solutions to enhance water contamination detection, particularly by leveraging machine learning algorithms and sensor networks for continuous monitoring.MethodsThis paper presents a novel AI-powered approach for improving water contamination detection, which incorporates real-time data processing and predictive modeling to identify contamination events and optimize response strategies. …”
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Enhancing blockchain transaction classification with ensemble learning approaches by Amrutanshu Panigrahi, Abhilash Pati, Bibhuprasad Sahu, Rourab Paul, Ajit Kumar Nayak, Subrata Chowdhury, Ramya Govindaraj, J Shreyas

“…Various ML classification algorithms are applied to the selected features from two ensemble feature selection algorithms as the base learners to make initial predictions. …”
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Ranking polycystic ovarian syndrome (PCOS) drugs using degree-based indices in QSPR models and CRITIC-driven MCDM methods by Muhammad Waheed Rasheed, Abid Mahboob, Laiba Amin, Aysha Hussain

“…An advanced graph-theory technique is used in the investigation of PCOS medication chemical structure prediction, with special focus on unique degree-based topological indices like the Banhatti and Zagreb indices. …”
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Design and Development of a Public AI Referee Assistance System Based on Harmony OS Platform by Jingjing Zhao, Chang Zhu, Bo Leng, Jiantao Qi

“…In this study, it was found that the object detection model trained in this study performed well, with a prediction accuracy of up to 99%. For posture recognition, this study also utilizes the relatively mature MediaPipe algorithm, which ensures high accuracy of the entire intelligent referee system’s ruling and meets the needs of current students. …”
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