An Investigation in Analyzing the Food Quality Well-Being for Lung Cancer Using Blockchain through CNN by Mohamed Abdelkader Aboamer, Mohamed Yacin Sikkandar, Sachin Gupta, Luis Vives, Kapil Joshi, Batyrkhan Omarov, Sitesh Kumar Singh

“…In this regard, “convolutional neural network” or CNN and blockchain are two important parts that together fasten the disease detection procedures securely. CNN can detect and predict diseases like lung cancer and help determine food quality, and blockchain is responsible for data. …”
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Weighted Hybrid Random Forest Model for Significant Feature prediction in Alzheimer’s Disease Stages by M. Rohini, D. Surendran

“…Abstract In recent studies, several machine learning and deep learning prediction models have been proposed for the early detection and classification of various stages of Alzheimer’s Disease (AD). …”
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Artificial intelligence driven mental health diagnosis based on physiological signals by P.R. Naregalkar, A.A. Shinde, M.V. Patil

“…This work uses a machine learning approach to detect a subject in stressed and non-stressed situations. …”
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Artificial intelligence analysis in cyber domain: A review by Liguo Zhao, Derong Zhu, Wasswa Shafik, S Mojtaba Matinkhah, Zubair Ahmad, Lule Sharif, Alisa Craig

“…The results in this study demonstrated that using a proportional collection of instances trained with the deep learning algorithm, a classified data set can accurately detect suspicious behavior. …”
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Using New Technologies to Analyze Gut Microbiota and Predict Cancer Risk by Mohammad Amin Hemmati, Marzieh Monemi, Shima Asli, Sina Mohammadi, Behina Foroozanmehr, Dariush Haghmorad, Valentyn Oksenych, Majid Eslami

“…Early detection is made easier by machine learning algorithms that improve the categorization of cancer kinds based on microbiological data. …”
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Recurrent Adaptive Classifier Ensemble for Handling Recurring Concept Drifts by Tinofirei Museba, Fulufhelo Nelwamondo, Khmaies Ouahada, Ayokunle Akinola

“…To learn streaming data, fast and accurate machine learning algorithms are needed for time-dependent applications. …”
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Tuberculosis Lesion Segmentation Improvement in X-Ray Images Using Contextual Background Label by Sahasat Khumang, Supaporn Kansomkeat, Wiwatana Tanomkiat, Sathit Intajag

“…Pulmonary tuberculosis (PTB) is a serious, potentially fatal, infectious disease that primarily affects the lungs, and poses a significant threat to public health. To detect PTB at an early stage by screening chest X-Ray (CXR) images for tuberculosis (TB) lesions, we propose a semantic segmentation scheme that uses a deep learning algorithm. …”
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Research on community characteristics of vegetation restoration in hilly power engineering based on multi temporal remote sensing technology by Chen Bin, Lei Dong, Huang Liangjun, Liu Qingdong, Zhao Xuan, Shi Yuanping

“…Combining the deep learning model with the multi model hierarchical classification algorithm, the remote sensing prediction map based on deep learning is used as the classification base map, and the normalized difference vegetation index threshold is used to classify the uncovered area. …”
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Artificial Intelligence in Cardiovascular Diagnosis: Innovations and Impact on Disease Screenings by Amber Ahmad, Sahil Ahmad, Rida Ahmad, Jahnavi Bodi, Abdulla Mohamed, Ahmad Wasim

“…Materials and methods: Various AI models as well as algorithms, such as machine learning (ML) and deep learning (DL) algorithms, have shown good results in the detection of diseases like heart failure, atrial fibrillation, coronary artery disease, and valvular heart disease. …”
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3D-CNN with Multi-Scale Fusion for Tree Crown Segmentation and Species Classification by Jiayao Wang, Zhen Zhen, Yuting Zhao, Ye Ma, Yinghui Zhao

“…This study combines deep learning with traditional image segmentation methods to improve individual tree crown detection and species classification. …”
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Drone-Based Digital Phenotyping to Evaluating Relative Maturity, Stand Count, and Plant Height in Dry Beans (Phaseolus vulgaris L.) by Leonardo Volpato, Evan M. Wright, Francisco E. Gomez

“…For early-stage SC assessment, Faster RCNN object detection algorithm was evaluated. Flight frequencies, image resolution, and data augmentation techniques were investigated to enhance DL model performance. …”
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Blockchain based solid waste classification with AI powered tracking and IoT integration by Aliaa M. Alabdali

“…AI algorithms, including machine learning (ML) and deep learning (DL), classify waste in real-time, optimizing waste collection and recycling. …”
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Prediction of Omicron Virus Using Combined Extended Convolutional and Recurrent Neural Networks Technique on CT-Scan Images by Anand Kumar Gupta, Asadi Srinivasulu, Kamal Kant Hiran, Goddindla Sreenivasulu, Sivaram Rajeyyagari, Madhusudhana Subramanyam

“…In this research work, two learning algorithms, namely, deep learning (DL) and machine learning (ML), were developed to forecast the Omicron virus infections. …”
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Development and application of an early prediction model for risk of bloodstream infection based on real-world study by Xiefei Hu, Shenshen Zhi, Yang Li, Yuming Cheng, Haiping Fan, Haorong Li, Zihao Meng, Jiaxin Xie, Shu Tang, Wei Li

“…This study aims to explore the optimal combination of routine laboratory data and clinical monitoring indicators, and to utilize machine learning algorithms to construct an early, rapid, and universally applicable BSI risk prediction model, to assist in the early diagnosis of BSI in clinical practice. …”
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Personalized prediction of negative affect in individuals with serious mental illness followed using long-term multimodal mobile phenotyping by Christian A. Webb, Boyu Ren, Habiballah Rahimi-Eichi, Bryce W. Gillis, Yoonho Chung, Justin T. Baker

“…Findings support the use of smartphones coupled with machine learning to detect states of heightened negative emotions. …”
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RivAIr: A custom-designed UAV-based sensor for real-time water area segmentation and surface velocity estimation by Marco La Salandra, Rosa Colacicco, Simone Panza, Giovanni Fumai, Pierfrancesco Dellino, Domenico Capolongo

“…This self-contained, edge-computing module integrates custom hardware with real-time machine learning and optical flow algorithms, enabling onboard water segmentation and river surface velocity estimation. …”
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Implementation of an Intelligent Trap for Effective Monitoring and Control of the <i>Aedes aegypti</i> Mosquito by Danilo Oliveira, Samuel Mafra

“…The application of machine learning (ML) algorithms and computer vision for the identification and classification of <i>Aedes aegypti</i> is a crucial part of this process. …”
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Identifying Asthma-Related Symptoms From Electronic Health Records Using a Hybrid Natural Language Processing Approach Within a Large Integrated Health Care System: Retrospective S... by Fagen Xie, Robert S Zeiger, Mary Marycania Saparudin, Sahar Al-Salman, Eric Puttock, William Crawford, Michael Schatz, Stanley Xu, William M Vollmer, Wansu Chen

“…Subsequently, transformer-based deep learning algorithms were trained using the same manually annotated datasets. …”
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Transparent and Robust Artificial Intelligence-Driven Electrocardiogram Model for Left Ventricular Systolic Dysfunction by Min Sung Lee, Jong-Hwan Jang, Sora Kang, Ga In Han, Ah-Hyun Yoo, Yong-Yeon Jo, Jeong Min Son, Joon-myoung Kwon, Sooyeon Lee, Ji Sung Lee, Hak Seung Lee, Kyung-Hee Kim

“…Recent advances in deep learning offer new opportunities to identify left ventricular systolic dysfunction (LVSD), a key indicator of HF, from ECG data. …”
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Wearable Artificial Intelligence for Sleep Disorders: Scoping Review by Sarah Aziz, Amal A M Ali, Hania Aslam, Alaa A Abd-alrazaq, Rawan AlSaad, Mohannad Alajlani, Reham Ahmad, Laila Khalil, Arfan Ahmed, Javaid Sheikh

“…The primary selection criterion was the inclusion of studies that utilized AI algorithms to detect or predict various sleep disorders using data from wearable devices. …”
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