An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection by Sarah Tipper, Hany F. Atlam, Harjinder Singh Lallie

“…CNNs enable the effective extraction of spatial features from video frames, such as facial textures and lighting, while LSTM analyses temporal patterns, detecting inconsistencies over time. This hybrid model enhances the ability to detect deepfakes by combining spatial and temporal analysis. …”
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Clinical and brain functional correlates of instrumental rigidity measurement in Parkinson’s disease by Yoshikazu Nakano, Shigeki Hirano, Mitsuyoshi Tamura, Yume Koizumi, Yoshihisa Kitayama, Michiko Izumi, Masahide Suzuki, Kosuke Yamagishi, Yutaro Suzuki, Shinobu Ikeda, Ayaka Arase, Tatsuya Yamamoto, Yoshitaka Yamanaka, Atsuhiko Sugiyama, Kazumoto Shibuya, Takuro Horikoshi, Takashi Uno, David Eidelberg, Satoshi Kuwabara

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Not seeing the trees for the forest. The impact of neighbours on graph-based configurations in histopathology by Olga Fourkioti, Matt De Vries, Reed Naidoo, Chris Bakal

“…By incorporating neighboring tiles into the analysis, we examined whether contextual information improves or impairs the network’s ability to identify patterns and features critical for accurate classification. …”
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A Novel Framework for Saraiki Script Recognition Using Advanced Machine Learning Models (YOLOv8 and CNN) by Hafiz Muhammad Raza Ur Rehman, Syed Arfan Haider, Hiba Faisal, Kook-Yeol Yoo, M. Z. Jhandir, Gyu Sang Choi

“…The study used Convolutional Neural Networks (CNNs) in conjunction with YOLOv8 models to address the problems of recognizing Saraiki alphabets’ primary and secondary components. …”
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Examining the Impact of Content Features on Customer Engagement in Social Media: A Data Mining Approach on Instagram by Azade Shekari, Mohammad Mehdi Poursaeed, Saeed Dehyadegari

“…Methodology Considering the huge amount of content produced on social networks, one of the best methods to analyze and identify patterns in these networks is data mining techniques. …”
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Comparative analysis of machine learning models for predicting water quality index in Dhaka’s rivers of Bangladesh by Mosaraf Hosan Nishat, Md. Habibur Rahman Bejoy Khan, Tahmeed Ahmed, Syed Nahin Hossain, Amimul Ahsan, M. M. El-Sergany, Md. Shafiquzzaman, Monzur Alam Imteaz, Mohammad T. Alresheedi

“…Furthermore, an Adjusted R 2 value of 0.965 further confirmed its ability to capture complex patterns in water quality data with remarkable accuracy. …”
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Enhancing prediction of crop yield and soil health assessment for sustainable agriculture using machine learning approach by Kapil Netaji Vhatkar, Shweta Ashish Koparde, Sonali Kothari, Jayesh Sarwade, Kishor Sakur

“…Back-propagation Neural Network (BPNN) is an SL algorithm applied for predicting complex patterns, especially in crop yield and soil health assessments. …”
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Diagnosis of array antennas based on near-field data using Faster R-CNN by Boguang Yang, Yulun Wei, Jixiang Shi, Tao Hong, Liangyu Li, Kai-Da Xu

“…The near-field pattern images of an array are collected, divided into training and test datasets, and then fed into the Faster R-CNN to learn the inverse operator of the EFIE formula. …”
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Integrated solar electrification and community empowerment in a burkina faso Village: A feasibility and design study by Bertrand Sawadogo, Amel Benmouna, Mohamed Becherif, Shimaa barakat, Mohamed Samy

“…This study combines spatial analysis through geographic information systems to enhance network layout and utilizes comprehensive load profiling to understand household energy demand patterns. …”
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Data-driven strategies for digital native market segmentation using clustering by Md Ashraf Uddin, Md. Alamin Talukder, Md. Redwan Ahmed, Ansam Khraisat, Ammar Alazab, Md. Manowarul Islam, Sunil Aryal, Ferdaus Anam Jibon

“…Our findings provide actionable insights into business impact and critical patterns driving future marketing growth. In our experiment, we systematically evaluate various clustering techniques, and notably, the Kmeans cluster outperforms others, demonstrating strong segmentation ability in the digital market. …”
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Iterative Assessment of Edge Criticality: Efficiency Enhancement or Hidden Insufficiency Detection by Vasily Lubashevskiy, Hamza Ejjbiri, Ihor Lubashevsky

“…Two main approaches are commonly used to rank edges based on their importance for maintaining network connectivity. The first is the Static approach, which relies on a single evaluation of topological features. …”
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SDN-Enabled IoT Security Frameworks—A Review of Existing Challenges by Sandipan Rakeshkumar Mishra, Bharanidharan Shanmugam, Kheng Cher Yeo, Suresh Thennadil

“…The review categorizes and analyzes these ML/DL implementations across various architectural paradigms, identifying patterns in their effectiveness for different security contexts. …”
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Long-Term, Multivariate Time Series Generation With the Capture of Intervariate Correlations and Variatewise Characteristics by Kasumi Ohno, Kohei Makino, Makoto Miwa, Yutaka Sasaki

“…Since existing TSG methods, particularly Generative Adversarial Networks (GANs)-based methods, focus on modeling long-term time series and capturing intervariate correlations, they often struggle to adequately capture the characteristics of each variate, such as specific patterns of waveforms. …”
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A Data-Driven Approach for Generating Synthetic Load Profiles with GANs by Tsvetelina Kaneva, Irena Valova, Katerina Gabrovska-Evstatieva, Boris Evstatiev

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Human activity-aware coverage path planning for robot-based mosquito control by Ash Yaw Sang Wan, Prabakaran Veerajagadheswar, Mohan Rajesh Elara, Anh Vu Le

“…In response to these challenges, this article proposes a novel human-first approach for complete coverage path planning (HFA-CCPP) that leverages the Glasius Bio-inspired Neural Network (GBNN) to cover areas that simulate and consider human activity patterns systematically. …”
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Urban resilience assessment framework and spatiotemporal dynamics in Hubei, China by Yan Liang, Yingsong Cheng, Fu Ren, Qingyun Du

“…Abstract Building resilient cities has become an emerging risk management strategy, thus it is necessary to make a scientific evaluation on urban resilience. In this study, both the Driving Force-Pressure-State-Impact-Response (DPSIR) framework and the BP neural network are innovatively adopted to construct a comprehensive urban resilience evaluation system. …”
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Identification of hub genes in myocardial infarction by bioinformatics and machine learning: insights into inflammation and immune regulation by Juan Yang, Xiang Li, Li Ma, Jun Zhang

“…The CIBERSORT algorithm was utilized to evaluate immune cell infiltration patterns. Finally, potential therapeutic targets were explored through drug-gene interaction analysis using the DGIdb database.ResultsAfter correcting for batch effects across datasets, we identified 687 differentially expressed genes (DEGs), including 405 upregulated and 282 downregulated genes. …”
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Modeling the Impact of Public Transportation and Mitigation Strategies in COVID-19 Spread in Mexico City by Fernando Reyes-Gomez, Emmanuel Torres-Marin, Maria Elena Larraga, Luis Alvarez-Icaza, Roman A. Mora-Gutierrez

“…Furthermore, the model incorporates several key time-varying parameters such as district population, commuting patterns, and contact matrices for different settings. …”
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Safeguarding against Cyber Threats: Machine Learning-Based Approaches for Real-Time Fraud Detection and Prevention by Vikas R. Shetty, Pooja R., Rashmi Laxmikant Malghan

“…In addition, deep learning techniques and artificial neural networks (ANN) are used to detect complex fraud patterns, while logistic regression is used to model the probability of fraudulent events. …”
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A Sparse Pooling Adversarial Learning Framework for Anomaly Event Detection by ZHANG, M., HU, H., LI, Z.

“…The test results demonstrate that the proposed method can effectively learn action patterns and accurately detect abnormal events in community scenarios.…”
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