Identification of Spambots and Fake Followers on Social Network via Interpretable AI-Based Machine Learning by Danish Javed, Noor Zaman Zaman, Navid Ali Khan, Sayan Kumar Ray, Arafat Al-Dhaqm, Victor R. Kebande

“…Social networking platforms like X (Twitter) serve as hubs for open human interaction, but they are also increasingly infiltrated by automated accounts masquerading as human users. …”
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Prevention and Management of Complications With a Hyaluronic Acid–Calcium Hydroxyapatite Hybrid Injectable by Philipp-Dormston WG, De Boulle K, Eccleston DB, Murthy R, Sayed K, Snozzi P, Urdiales-Galvez F

“…Small group workshops also shared management algorithms for late noninflammatory adverse reactions, late inflammatory adverse reactions, localized vascular complications, and retinal vascular complications. …”
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Machine Learning in Cyber-Physical Systems and Manufacturing Singularity – it Does Not Mean Total Automation, Human Is Still in the Centre: Part I – Manufacturing Singularity and a... by Goran D. PUTNIK, Vaibhav SHAH, Zlata PUTNIK, Luis FERREIRA

“…The hypothesis presented in this paper is that there is a limit of AI/ML autonomy capacity, and more concretely, that the ML algorithms will be not able to became totally autonomous and, consequently, that the human role will be indispensable. …”
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Machine learning for the prediction of mortality in patients with sepsis-associated acute kidney injury: a systematic review and meta-analysis by Xiangui Lv, Daiqiang Liu, Xinwei Chen, Lvlin Chen, Xiaohui Wang, Xiaomei Xu, Lin Chen, Chao Huang

“…The results showed that the machine learning algorithms had a good performance in predicting sepsis-related acute kidney injury death prediction. …”
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Artificial Intelligence–Aided Diagnosis System for the Detection and Classification of Private-Part Skin Diseases: Decision Analytical Modeling Study by Wei Wang, Xiang Chen, Licong Xu, Kai Huang, Shuang Zhao, Yong Wang

“…Compared with existing advanced algorithms, this system is more accurate in identifying PPSDs. …”
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Explainable Siamese Neural Networks for Detection of High Fall Risk Older Adults in the Community Based on Gait Analysis by Christos Kokkotis, Kyriakos Apostolidis, Dimitrios Menychtas, Ioannis Kansizoglou, Evangeli Karampina, Maria Karageorgopoulou, Athanasios Gkrekidis, Serafeim Moustakidis, Evangelos Karakasis, Erasmia Giannakou, Maria Michalopoulou, Georgios Ch Sirakoulis, Nikolaos Aggelousis

“…Results: Through the extraction of distinctive gait-related features and the generation of class-discriminative activation maps using Grad-CAM, the random forest (RF) machine learning (ML) model not only achieves commendable accuracy (83.29%) but also enhances explainability. Conclusions: Ultimately, this study underscores the potential of advanced computational tools and machine learning algorithms to improve fall risk prediction, reduce healthcare burdens, and promote greater independence and well-being among the older adults.…”
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The NavINST Dataset for Multi-Sensor Autonomous Navigation by Paulo Ricardo Marques De Araujo, Eslam Mounier, Qamar Bader, Emma Dawson, Shaza I. Kaoud Abdelaziz, Ahmed Zekry, Mohamed Elhabiby, Aboelmagd Noureldin

“…It offers rich, multi-sensor data ideal for developing and validating robust algorithms for autonomous vehicles. Finally, it is fully integrated with the robot operating system (ROS), ensuring ease of use and accessibility for the research community. …”
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Resampling-driven machine learning models for enhanced high streamflow forecasting by Nureehan Salaeh, Sirimon Pinthong, Warit Wipulanusat, Uruya Weesakul, Jakkarin Weekaew, Quoc Bao Pham, Pakorn Ditthakit

“…SVR demonstrated the highest sensitivity to resampling adjustments, particularly when paired with SVMSMOTE and Org-Resampling methods. KNN also exhibited notable improvements under several Org-Resampling strategies. …”
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Physics-Based SPICE-Compatible Compact Model of FLASH Memory With Poly-Si Channel for Computing-in-Memory Applications by Jung Rae Cho, Donghyun Ryu, Donguk Kim, Wonjung Kim, Yeonwoo Kim, Changwook Kim, Yoon Kim, Myounggon Kang, Jiyong Woo, Dae Hwan Kim

“…The focus is to maximize the computing performance and design FLASH memory suitable for various AI algorithms, where the memory must achieve a highly controllable multi-level threshold voltage (VT). …”
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Recent Advances in Demand Responsive Transport: Opportunities With Autonomous Bus Service—A System-of-Systems Overview by Sajid Fadlelseed, Pouria Sarhadi, Soodamani Ramalingam, Hock C. Gan, Pandelis Kourtessis, Gav Jackman, Atziri Guadalupe Sanchez Contreras, Javier Guimera Tena, James West, Jack Holland, Alice Missler

“…Key components of modern DRT systems are categorised from multiple perspectives, including vehicle types, infrastructure, devices, simulation approaches, decision-making algorithms, and optimisation factors. Each element is discussed in detail, supported by comparative tables to help readers interpret the results effectively. …”
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CNN-Based Detection of Sheath Incorrect Connection in the Cross-Bonded HV Cable Systems Using the Sheath Current Phasor Difference by Jing Tu, Zhanran Xia, Yongheng Ai, Ruoxin Song, Bin Yang, Changqun Sun, Xiaoyue Chi, Hang Wang

“…Through comparison with other algorithms, it is found that the CNN exhibits the best performance in identifying various cross-bonding error scenarios.…”
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Fast, Accurate, and Low-Disturbance Automatic Bias Control for Coherent Optical Transmitter Using Dither Vector Mapping Monitoring by Hongyu Li, Xinghan Li, Mengfan Cheng, Qi Yang, Ming Tang, Deming Liu, Lei Deng

“…Therefore, it not only induces lower nonlinear distortion in the transmitted signal but also has strong noise resistance, contributing to good control accuracy and robustness. …”
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Interpretable machine learning for predicting isolated basal septal hypertrophy. by Lei Gao, Boyan Tian, Qiqi Jia, Xingyu He, Guannan Zhao, Yueheng Wang

“…Compared to traditional algorithms, machine learning algorithms are more effective at capturing nonlinear relationships and developing more accurate diagnostic and predictive models. …”
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AIPerf: Automated Machine Learning as an AI-HPC Benchmark by Zhixiang Ren, Yongheng Liu, Tianhui Shi, Lei Xie, Yue Zhou, Jidong Zhai, Youhui Zhang, Yunquan Zhang, Wenguang Chen

“…The plethora of complex Artificial Intelligence (AI) algorithms and available High-Performance Computing (HPC) power stimulates the expeditious development of AI components with heterogeneous designs. …”
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A Human-Centric, Uncertainty-Aware Event-Fused AI Network for Robust Face Recognition in Adverse Conditions by Akmalbek Abdusalomov, Sabina Umirzakova, Elbek Boymatov, Dilnoza Zaripova, Shukhrat Kamalov, Zavqiddin Temirov, Wonjun Jeong, Hyoungsun Choi, Taeg Keun Whangbo

“…Face recognition systems often falter when deployed in uncontrolled settings, grappling with low light, unexpected occlusions, motion blur, and the degradation of sensor signals. Most contemporary algorithms chase raw accuracy yet overlook the pragmatic need for uncertainty estimation and multispectral reasoning rolled into a single framework. …”
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Developing a semi-automated technique of surface water quality analysis using GEE and machine learning: A case study for Sundarbans by Sheikh Fahim Faysal Sowrav, Sujit Kumar Debsarma, Mohan Kumar Das, Khan Mohammad Ibtehal, Mahfujur Rahman, Noshin Tabassum Hridita, Atika Afia Broty, Muhammad Sajid Anam Hoque

“…Key water quality parameters—Sea Surface Temperature (SST), Total Suspended Solids (TSS), Turbidity, Salinity, and pH—were predicted through ML algorithms and interpolated using the Empirical Bayesian Kriging (EBK) model in ArcGIS Pro. …”
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Pharmacologic Disruption: How Emerging Weight Loss Therapies Are Challenging Bariatric Surgery Guidelines by Safi G. Alqatari, Abrar J. Alwaheed, Manal A. Hasan, Reem J. Al Argan, Marj M. Alabdullah, Mohammed D. Al Shubbar

“…We also discuss future directions in obesity management, including personalized treatment strategies, perioperative drug use, and the integration of pharmacologic agents into long-term care pathways. …”
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MOF-MoS2 nanosheets doped PEDOT:PSS for organic electrochemical transistors in enhanced glucose sensing and machine learning-based concentration prediction by Yali Sun, Yun Li, Yang Zhou, Ting Cai, Yuxuan Chen, Chao Zou, Han Song, Shenghuang Lin, Shenghua Liu

“…Finally, we illustrate the merits of integration machine learning algorithms to construct predictive models using the extensive datasets produced by our sensors for both classification and quantification tasks. …”
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Expose flexible conformations for intrinsically disordered protein by Jiaan Yang, Wenxin Ji, Wen Xiang Cheng, Gang Wu, Si Tong Sheng, Peng Zhang, Jun Lin, Xiaojia Chen, Qiong Shi

“…The folding conformation of native protein has flexibility in different degrees, which may bring difficulty in presenting the structures, and also it causes complexity in understanding the relationship between structure and functions. …”
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Leveraging dendritic complexity for neuromorphic computing by Suma G Cardwell, Mark Plagge, Luke Parker, Claire E Plunkett, David Munkvold, Paloma T Gonzalez-Bellido, Scott Koziol, Conrad James, Frances S Chance

“…We will present examples of neuroscience-inspired direction-selective circuits and an ANN with active dendrites leveraging shunting inhibition. We also demonstrate the benefits of using dendrites in deep neural networks. …”
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