Improving detection of Parkinson’s disease with acoustic feature optimization using particle swarm optimization and machine learning by Elmoundher Hadjaidji, Mohamed Cherif Amara Korba, Khaled Khelil

“…Leveraging voice analysis and classification techniques, numerous studies explore the feasibility of early PD detection through automated systems. While several databases offer acoustic features for this purpose, their effectiveness largely depends on the classification methodology employed. …”
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Towards Transparent Diabetes Prediction: Combining AutoML and Explainable AI for Improved Clinical Insights by Raza Hasan, Vishal Dattana, Salman Mahmood, Saqib Hussain

“…This study integrates Automated Machine Learning (AutoML) with Explainable Artificial Intelligence (XAI) to improve diabetes risk prediction and enhance model interpretability for healthcare professionals. …”
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Research on Mandatory Lane-Changing Behavior in Highway Weaving Sections by Wei Hao, Zhaolei Zhang, Zhibo Gao, Kefu Yi, Li Liu, Jie Wang

“…As the accident-prone sections and bottlenecks, highway weaving sections will become more complicated when it comes to the mixed-traffic environments with connected and automated vehicles (CAVs) and human-driven vehicles (HVs). …”
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Empirical case study of a digitally enabled energy community with prosumers and P2P trading by Philipp Staudt, Bent Richter

“…One direction to make energy communities more attractive to regulators and utilities is the conceptualization, design, and empirical evaluation of systems that lead to low perceived complexity for participants while enabling high levels of external automated control.…”
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Evolving techniques in sentiment analysis: a comprehensive review by Mahander Kumar, Lal Khan, Hsien-Tsung Chang

“…Sentiment analysis, a specialized field within natural language processing, has evolved to meet these challenges by automating the detection and categorization of opinions and emotions in text. …”
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Emerging technologies and language learning: mining the past to transform the future by Hubbard Philip

“…Today’s emerging technologies—artificial intelligence, machine learning, conversational robots, virtual worlds, virtual reality, augmented reality, automated assessment, and so on—are full of promise and seem poised to revolutionize language teaching and learning over the next decade. …”
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Partition Learning for Multiagent Planning by Jared Wood, J. Karl Hedrick

“…Automated surveillance of large geographic areas and target tracking by a team of autonomous agents is a topic that has received significant research and development effort. …”
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Analysis of The Role of Deep Learning Models in Image Classification Applications by Li Xiang

“…The integration of deep learning models, particularly Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs), has revolutionized this process, enabling the development of automated, fast, and practical systems. These advanced models are now employed in diverse areas, including biomedical science, remote sensing, and business management, thanks to their ability to achieve high accuracy across a broad spectrum of scenarios. …”
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A shallow convolutional neural network for cerebral neoplasm detection from magnetic resonance imaging by Hossein Sadr, Zeinab Khodaverdian, Mojdeh Nazari, Mohammad Yamaghani

“…The result of this study not only enables healthcare professionals to quickly and accurately identify brain malignancies but also automates the diagnostic process and minimizes dependence on manual interpretation. …”
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Lung Cancer Prediction Using Neural Network Ensemble with Histogram of Oriented Gradient Genomic Features by Emmanuel Adetiba, Oludayo O. Olugbara

“…The result of the ANN ensemble and HOG genomic features is promising for automated screening and early detection of lung cancer. …”
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Hematological Changes in Sudanese Patients with Falciparum Malaria Attending Elnihoud Teaching Hospital by Abdelnassir M. Ahamed, Hafiz Ahmed Hobiel, Gad Allah Modawe, Mohammed Saeed Elsammani

“…Questionnaire was filled by every participant and thick and thin blood films for malaria were prepared and stained by giemsa stain and the CBC was done by sysmex automated hematological analyzer. Results: Thrombocytopenia, leucopenia, microcytic hypochromic red blood cells and relative lymphocytosis have significant association with falciparum malaria with Pvalues 0.008, 0.001, 0.008, and 0.004, respectively. …”
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Sensitivity and Resolution Capacity of Electrode Configurations by Cyril Chibueze Okpoli

“…The parameters appreciate the effectiveness of automated multichannel system which has evolved several electrode arrays that are cost effective, reduction in survey time, high sensitivity, and resolution capacity in 2D and 3D resistivity tomographies. …”
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Hematoxylin and Eosin-stained whole slide image dataset annotated for skin tissue segmentationMendeley Data by Anum Abdul Salam, Muhammad Zeeshan Asaf, Muhammad Usman Akram, Noah Musolff, Samavia Khan, Bassem Rafiq, Babar Rao

“…With the advent of Artificial Intelligence (AI) and technology, the use of automated disease diagnosis systems has increased significantly. …”
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A multicenter study of neurofibromatosis type 1 utilizing deep learning for whole body tumor identification by Cheng-Jiang Wei, Yan Tang, Yang-Bai Sun, Tie-Long Yang, Cheng Yan, Hui Liu, Jun Liu, Jing-Ning Huang, Ming-Han Wang, Zhen-Wei Yao, Ji-Long Yang, Zhi-Chao Wang, Qing-Feng Li

“…Using neurofibromatosis type 1 (NF1) as an example, this study developed highly accurate MRI-based deep-learning models for the early automated screening of malignant peripheral nerve sheath tumors (MPNSTs) against complex whole-body background. …”
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Vibration Based Diagnosis for Planetary Gearboxes Using an Analytical Model by Liu Hong, Yongzhi Qu, Yuegang Tan, Mingyao Liu, Zude Zhou

“…This analytical model is easy to implement because the only prerequisite information is the basic geometry of the planetary gear-set. Afterwards, an automated diagnostic scheme is proposed to cope with the challenges associated with the characteristic configuration of planetary gearboxes. …”
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Holistic Framework for Blockchain-Based Halal Compliance in Supply Chains Enabled by Artificial Intelligence by Funlade Sunmola, George Baryannis, Albert Tan, Kenneth Co, Emmanuel Papadakis

“…This paper proposes a holistic framework for halal certification and compliance, addressing these challenges through the integration of artificial intelligence (AI) and blockchain technologies. AI can automate halal compliance checks, identify potential irregularities in sourcing and composition, and facilitate risk management. …”
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V2Va +: An Efficient SystemVerilog & Verilog-to-Verilog-A Translator for Accelerated Mixed-Signal Simulation by Chao Wang, Yicong Shao, Jiajie Huang, Wangzilu Lu, Zhiwen Gu, Longfan Li, Yuhang Zhang, Jian Zhao, Wei Mao, Yongfu Li

“…This paper introduces a streamlined SystemVerilog & Verilog-to-Verilog-A (V2Va +) translation tool that automates the conversion of synthesizable SystemVerilog and Verilog code into Verilog-A code, enabling concurrent simulation of analog and digital circuits. …”
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Utilizing FRET-based Biosensors to Measure Cellular Phosphate Levels in Mycorrhizal Roots of Brachypodium distachyon by Shiqi Zhang, Lucas Jurgensen, Maria Harrison

“…A complementary control sensor, cpFLIPPi-Null, is introduced to monitor non-Pi-specific changes. Leveraging a semi-automated ImageJ macro for sensitized FRET analysis, this method provides a precise and efficient way to determine relative intracellular Pi levels at the level of individual cells or organelles.…”
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QPPLab: A generally applicable software package for detecting, analyzing, and visualizing large-scale quasiperiodic spatiotemporal patterns (QPPs) of brain activity by Nan Xu, Behnaz Yousefi, Nmachi Anumba, Theodore J. LaGrow, Xiaodi Zhang, Shella Keilholz

“…QPPLab integrates correlation-based iterative algorithms, supports customizable parameter settings, and features automated workflows to simplify analysis. Processing times vary depending on dataset size and the selected mode, with the fast detection mode completing analyses that can be 4–6 times faster than the robust detection mode. …”
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Deep Learning Architecture to Infer Kennedy Classification of Partially Edentulous Arches Using Object Detection Techniques and Piecewise Annotations by Zohaib Khurshid, MRes, FDTFEd, FHEA, Maria Waqas, PhD, Shehzad Hasan, PhD, Shakeel Kazmi, PhD, Muhammad Faheemuddin, FCPS

“…This research work focuses on utilizing artificial intelligence and deep learning–based object detection techniques for automated detection of common dental issues in orthopantomography x-ray images, including broken roots, periodontally compromised teeth, and the Kennedy classification of partially edentulous arches. …”
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