ML-Enabled Millimeter-Wave Software-Defined Radio With Programmable Directionality by Marc Jean, Murat Yuksel, Xun Gong

“…We design and implement Q-learning, Double Q-learning, and Deep Q-learning algorithms that passively inspect the Received Signal Strength (RSS) of the mmWave beam and autonomously determine the predicted AoA. …”
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AgrUNet: A Multi-GPU UNet Based Model for Crops Classification by Andrea Miola, Enrico Calore, Sebastiano Fabio Schifano

“…The deployment of high-resolution satellite missions, like LandSat and Copernicus Sentinel, combined with AI Deep Learning (DL) methodologies has revolutionized Earth Observation science, enabling studies on yield predictions, soil classifications, and crop mappings on large areas, and the analysis and processing of Big Data using innovative approaches. …”
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Evaluation of underground hard rock mine pillar stability using gene expression programming and decision tree‐support vector machine models by Mohammad H. Kadkhodaei, Ebrahim Ghasemi, Jian Zhou, Melika Zahraei

“…This study mainly focuses on developing two practical models to predict pillar stability status. For this purpose, two robust models were developed using a database including 236 case histories from seven underground hard rock mines, based on gene expression programming (GEP) and decision tree‐support vector machine (DT‐SVM) hybrid algorithms. …”
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View Score: An early warning score to detect possible complications among COVID-19 patients by Meenakshi Bhakare, Rajkumar Nikalje, Reshma Patil, Urvi Shukla, Rupesh Bokade, Gajanan Sakhare, Shardul Joshi, Aditi Pais

“…Conclusion: Home-based spirometry acts as an effective tool for COVID-19 patients to predict lung complications and also promote self-monitoring thereby reducing the burden on the health system.…”
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Computational identification of phospho-tyrosine sub-networks related to acanthocyte generation in neuroacanthocytosis. by Lucia De Franceschi, Giovanni Scardoni, Carlo Tomelleri, Adrian Danek, Ruth H Walker, Hans H Jung, Benedikt Bader, Sara Mazzucco, Maria Teresa Dotti, Angela Siciliano, Antonella Pantaleo, Carlo Laudanna

“…Here, we coupled tyrosine-phosphoproteomic analysis to topological network analysis. We aimed to predict signaling sub-networks possibly involved in the generation of acanthocytes in patients affected by the two core NA disorders, namely McLeod syndrome (MLS, XK-related, Xk protein) and chorea-acanthocytosis (ChAc, VPS13A-related, chorein protein). …”
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Early detection of bloodstream infection in critically ill children using artificial intelligence by Hye-Ji Han, Kyunghoon Kim, June Dong Park

“…Conclusions We developed a machine learning model that predicts BSI with acceptable performance. Further research is necessary to validate its effectiveness.…”
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Understanding Acceleration-Based Load Metrics: From Concepts to Implementation by João Freitas, Alexandre Moreira, João Carvalho, Filipe Conceição, Luisa Estriga

“…In time-series comparisons, Jerk Modulus and cAccel’Rate were predictably found to be nearly identical, while Body Load was the most distinct. …”
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Stability indicators in network reconstruction. by Michele Filosi, Roberto Visintainer, Samantha Riccadonna, Giuseppe Jurman, Cesare Furlanello

“…The number of available algorithms to infer a biological network from a dataset of high-throughput measurements is overwhelming and keeps growing. …”
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Edge Artificial Intelligence Device in Real-Time Endoscopy for Classification of Gastric Neoplasms: Development and Validation Study by Eun Jeong Gong, Chang Seok Bang, Jae Jun Lee

“…Objective: We previously developed artificial intelligence (AI) diagnosis algorithms for predicting the six classes of stomach lesions. …”
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Smart city planning focused on the US cities in need of policing innovations and public health safety technologies and strategies by Delores Springs

“…In particular, the article proposes the following for implementation: identifying crime hotspots by means of advanced geographic information systems, applying machine learning algorithms to predict future crime trends, using advanced real-time surveillance technologies and firearms detection systems, applying geospatial analysis to optimise patrol routes, dynamically adjusting patrol routes depending on real-time data analytics, deploying mobile command centres. …”
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Artificial Intelligence in Pathology: Past, Present, and Future Perspectives by Isma Balaj, Sibora Bërdica, Teona Bushati

“…Examples currently exist providing diagnostic support for a limited but growing number of applications, such as tumor detection, automated tumor grading, immunohistochemistry scoring, and predicting mutation status. Several challenges remain, including the validation and establishment of a regulatory framework for these tools, as well as the ethical implications of AI in pathology. …”
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Advancing invasive species monitoring: A free tool for detecting invasive cane toads using continental-scale data by Franco Ka Wah Leung, Lin Schwarzkopf, Slade Allen-Ankins

“…We validated thousands of BirdNET predictions across Australia, and our classifier achieved over 90 % accuracy even at many sites outside the areas from which the training data were obtained. …”
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Use of Artificial Intelligence in Imaging Dementia by Manal Aljuhani, Azhaar Ashraf, Paul Edison

“…A convolutional neural network method was developed, and external validation predicted final clinical diagnoses of Alzheimer’s disease, dementia with Lewy bodies, mild cognitive impairment due to Alzheimer’s disease, or cognitively normal with FDG-PET. …”
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Diagnosing Autoimmune Bullous Diseases—An Indian Perspective by Adhyatm Bhandari, Dipankar De, Shikha Shah, Debajyoti Chatterjee, Vinod Kumar, Rahul Mahajan, Sanjeev Handa

“…Accurate diagnosis of the specific subtype of AIBD is crucial for effective management and predicting prognosis, especially in cases with an increased risk of malignancy. …”
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Artificial Intelligence Driven Human Identification by Purushottam Sharma, Aazar Imran Khan, Samyak Jain, Abhishek Srivastava

“…Gait (i.e., an individual’s unique walking pattern/style) is a leading exponent when compared to first-generation biometric modalities as it is unobtrusive (i.e., it requires no contact with the individual), hence proving gait to be an optimal solution to human identification at a distance.This paper proposes an automatic identification system that analyzes gait to identify humans at a distance and predicts the strength of the match (i.e., probability of the match being positive) between two gait profiles. …”
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Cross-Network User Identity Linkage Method with Deep Learning Based on SDNE Embedding Representation by CHENG Jialin, YUAN Deyu, SUN Zeyu, CHEN Ziyan

“…Experimental verification is conducted on the real social network dataset and synthetic network datasets, and the experimental results are more than 8 percentage points higher than the baseline algorithms PALE (predict anchor links via embedding), CLF (collective link fusion) and Deeplink in accuracy and F1 value, indicating that the eSUIL algorithm proposed in this paper has excellent performance in user identity linkage. …”
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Harnessing AI and Quantum Computing for Revolutionizing Drug Discovery and Approval Processes: Case Example for Collagen Toxicity by David Melvin Braga, Bharat Rawal

“…By generating computational data, predicting the efficacy of pharmaceuticals, and assessing their safety, AI and quantum computing can accelerate and optimize the process of identifying potential drug candidates. …”
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Optimizing Small Wind Turbine Blades: A BEMT Approach Optimizing Small Wind Turbine Blades: A BEMT Approach by Tshepo Sithole, Lukas W. Snyman, Vasudeva R. Veeredhi, Thembelani Sithebe

“…Results indicate that at an average wind speed of 0 - 2.3 m/s (8.28 km/h), 3-blade, 5-blade, and 7-blade sets were designed and optimized for performance. The predictions suggest rated outputs of 7.5 W, 20 W, and 40 W for Designs 1, 2, and 3, respectively. …”
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Overcoming the challenges of data integration in ecosystem studies with machine learning workflows: an example from the Santos project by Gustavo Fonseca, Danilo Candido Vieira

“…These challenges encompass defining a conceptual model outlining cause-and-effect relationships, addressing dissimilarities in data source quantity and information content, grappling with missing or noisy data, fine-tuning model optimization, achieving accurate predictions, and tackling the issue of imbalanced observations across factors. …”
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Evaluating Recycling Initiatives for Landfill Diversion in Developing Economies Using Integrated Machine Learning Techniques by Muyiwa Lawrence Adedara, Ridwan Taiwo, Olusola Olaitan Ayeleru, Hans-Rudolf Bork

“…It evaluates the recycling diversion rate (RDR) of household recyclables (HSRs) across local government areas using field surveys and population data. Machine learning algorithms (logistic regression, random forest, XGBoost, and CatBoost) refined with Bayesian optimisation were employed to predict household recycling motivation. …”
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