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PARKINSON'S DISEASE CLASSIFICATION USING HYBRID NAMIB SQUIRREL SEARCH WATERa* algorithm-BASED DEEP LEARNING APPROACH

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INTERNATIONAL JOURNAL FOR MULTISCALE COMPUTATIONAL ENGINEERING 2025年第3期23卷 37-61页

作者： Sharanyaa, S. Mathew, Jimsha K. Nair, Kavitha R. Arumugasamy, Muthulakshmi Panimalar Engn Coll Dept Informat Technol Chennai 600056 Tamil Nadu India New Horizon Coll Engn Dept Artificial Intelligence & Machine Learning Bengaluru 560103 Karnataka India Acharya Inst Technol Dept Artificial Intelligence & Machine Learning Bengaluru 560107 Karnataka India Sathyabama Inst Sci & Technol Dept Comp Sci & Engn Chennai 600119 Tamil Nadu India

*Address all correspondence to: Department of Artificial Intelligence and Machine Learning, New Horizon Parkinson's disease (PD) is a neurological disease throughout the globe, and it is the second leading reason for death and impairment. The overall cases of PD have nearly doubled in the past 15 years. It has been defined by the absence of dopamine cells in the brain. PD affects about 1% of individuals over the age of 65, while 90% of them are affected by speech disorders like articulation, phonation, fluency, and prosody. Hence, the earlier prediction is significant in providing a good treatment for PD. In this research, the Namib squirrel search watera* algorithm (NSSWA) is proposed for PD classification. The voice sample is used as input and it is preprocessed using a Gaussian filter. Furthermore, feature extraction is applicable for the extraction of significant features, and the feature selection is done using the NSSWA. Moreover, the NSSWA-trained convolutional neural network (CNN) fused long short-term memory (LSTM) (CNN-LSTM), called NSSWA_CNN-LSTM, is used in PD classification. In addition, the efficacy of the model is validated via accuracy, sensitivity, specificity, loss function, mean-square error, and root-mean-square error with optimal values of 0.931, 0.934, 0.929, 0.068, 0.097, and 0.312 obtained.

关键词： KEY WORDS: convolutional neural network Parkinson's disease Namib beetle optimization algorithm long short term memory squirrel searcha* algorithm

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Quantifying and improving rheumatoid arthritisa* algorithm performance in biobank settings

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SEMINARS IN ARTHRITIS AND RHEUMATISM 2025年 72卷 152668-152668页

作者： Kronzer, Vanessa L. Williamson, Katrina A. Hanson, Andrew C. Sletten, Jennifer A. Sparks, Jeffrey A. Davis III, John M. Crowson, Cynthia S. Mayo Clin Div Rheumatol Rochester MN USA Mayo Clin Dept Quantitat Hlth Sci Rochester MN USA Harvard Med Sch Brigham & Womens Hosp Div Rheumatol Inflammat & Immun Boston MA USA

Objective: To quantify and improve the performance of standard rheumatoid arthritis (RA)a* algorithms in a biobank setting. Methods: This retrospective cohort study within the Mayo Clinic (MC) Biobank and MC Tapestry Study identified RA cases by presence of at least two RA codes OR positive anti-cyclic citrullinated peptide antibodies (CCP) plus disease-modifying anti-rheumatic drug (DMARD) prescription as of 7/18/2022. Rheumatology physicians manually verified all RA cases using RA criteria and/or rheumatology physician diagnosis plus DMARD use. All other biobank participants served as non-RA controls. We defined seropositivity as rheumatoid factor and/or anti-CCP positivity. We assessed rules-based and Electronic Medical Records and Genomics (eMERGE) Ra* algorithms using positive predictive value (PPV). Finally, we developed a novel Ra* algorithm using a LASSO-based machine learning approach with five-fold cross validation. Results: We identified 1,316 confirmed RA cases (968 MC Biobank, 348 Tapestry, 70 % seropositive) and 82,123 non-RA controls (mean age 65, 61 % female). The PPV of 3 RA codes was 43 %, codes plus DMARD was 54 %, and codes plus DMARD plus seropositivity was 85 %. The PPV of eMERGE was 77 %. Available in the MC Biobank, self-reported RA (PPV 10 %) only minimally improveda* algorithm performance (PPV from 83 % to 85 %), whereas family history of RA (PPV 3 %) worsened performance. At 90 % PPV, the novel Ra* algorithm incorporating key variables such as anti-CCP and DMARD use increased sensitivity by 4-11 % compared to eMERGE. Conclusion: Rules-based and eMERGE Ra* algorithms had worse performance in biobank than administrative settings. Our novel Ra* algorithm outperformed these standarda* algorithms.

关键词： Rheumatoid arthritis algorithm Biobank Anti-CCP

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The Animated Oat Optimizationa* algorithm: A nature-inspired metaheuristic for engineering optimization and a case study on Wireless Sensor Networks

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KNOWLEDGE-BASED SYSTEMS 2025年 318卷

作者： Wang, Ruo-Bin Hu, Rui-Bin Geng, Fang-Dong Xu, Lin Chu, Shu-Chuan Pan, Jeng-Shyang Meng, Zhen-Yu Mirjalili, Seyedali North China Univ Technol Sch Informat Sci & Technol Beijing 100144 Peoples R China Southwest Jiaotong Univ State Key Lab Rail Transit Vehicle Syst Chengdu 610031 Peoples R China Univ Adelaide Sch Comp & Math Sci Adelaide 5005 Australia Nanjing Univ Informat Sci & Technol Sch Artificial Intelligence Nanjing 210044 Peoples R China Fujian Univ Technol Sch Comp Sci & Math Fuzhou 350118 Peoples R China Torrens Univ Australia Ctr Artificial Intelligence Res & Optimisat Brisbane Australia Obuda Univ Univ Res & Innovat Ctr H-1034 Budapest Hungary

In this paper, a novel metaheuristica* algorithm called the Animated Oat Optimizationa* algorithm (AOO) is proposed, inspired by the natural behavior of Animated Oat in the environment. AOO simulates 3 unique behaviors of Animated Oat: (i) seed dispersal through natural elements such as wind, water, and animals;(ii) under the influence of hygroscopic movement, the primary awn of Animated Oat seeds undergoes distortion and rotation, enabling the entire seed to roll and propagate;and (iii) during the rolling propagation, energy is stored upon encountering obstacles, triggering a propulsion mechanism under certain conditions to further disperse the seeds. To evaluate thea* algorithm's capabilities in exploration and exploitation, we utilized the CEC2022 test suite, which comprises 12 functions. Comparative analysis with 9 well-known optimizationa* algorithms demonstrates that AOO exhibits superior competitiveness. Furthermore, we extend our evaluation to five widely-used engineering design problems to confirm thea* algorithm's performance in these domains. Finally, we combined AOO with DV-Hop to validate its competitiveness and effectiveness in experiments on node localization in 3-dimensional Wireless Sensor Networks. These results demonstrate AOO's ability to tackle complex optimization challenges and its potential as a reliable optimizer for practical engineering applications. Source codes of AOA are publicly available at https://***/robingit77/Animated-Oat-Optimization-algorithm-AOO- and https ://***/morealgorithms.

关键词： Animated Oat Optimizationa* algorithm Seed dispersal mechanism Hygroscopic movement Wireless Sensor Networks Swarm intelligence algorithm Optimization Benchmark

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Development of ana* algorithm impacting COPD care through personalized nutrition and IoT-based monitoring

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JOURNAL OF HEALTH POPULATION AND NUTRITION 2025年第1期44卷 1-10页

作者： Sharma, Prachi Gharia, Mihir Aswal, Devina Trivedi, Vishva Soni, Bhavnita Soni, Prerna Joshi, Devesh Lalan, Dhvni Athavale, Vrushali Shaikh, Irfan Jacob, Koshy Tatvacare Ahmadabad India

BackgroundChronic obstructive pulmonary disease (COPD) is a chronic respiratory condition characterized by high morbidity and mortality rates. This study aims to assess the clinical outcomes of COPD patients after implementing ana* algorithm within the MyTatva *** study involved a sample of 10 COPD patients, evaluating key parameters such as Forced Expiratory Volume in 1 s (FEV1), Forced Vital Capacity, Weight, Body Mass Index (BMI), Fat-Free Mass Index, and Distance Covered during the 6-Minute Walk Test (6MWT) before and after thea* algorithm's implementation in the MyTatva app. Patient satisfaction was assessed through a CSAT *** the implementation of the MyTatva care plan, significant improvements were observed in several key clinical outcomes for COPD patients. FEV1 increased from a median of 3.24-2.0 L (p = 0.0379), while weight and BMI decreased significantly, with a reduction in weight from a median of 86-70 kg (p = 0.0007) and a corresponding decrease in BMI from 28.43 to 24 kg/m2 (p = 0.0031). The distance covered during the 6MWT also improved from 420 to 568 m (p = 0.0019). The participation of 10 COPD patients in surveys yielded an overall CSAT score of 85%, indicating a high level of satisfaction with the MyTatva *** comprehensive features and functionalities of the MyTatva app, combined with the personalized care plan and real-time feedback mechanisms, have led to substantial clinical improvements in COPD management. These findings highlight the promise of this innovative digital therapeutic approach in addressing chronic respiratory conditions.

关键词： COPD Diet Nutrition Digital therapeutics algorithm MyTatva

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Development and validation of ana* algorithm for identifying patients undergoing dialysis from patients with advanced chronic kidney disease

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CLINICAL AND EXPERIMENTAL NEPHROLOGY 2025年第5期29卷 650-661页

作者： Imaizumi, Takahiro Yokota, Takashi Funakoshi, Kouta Yasuda, Kazushi Hattori, Akiko Morohashi, Akemi Kusakabe, Tatsumi Shojima, Masumi Nagamine, Sayoko Nakano, Toshiaki Huang, Yong Morinaga, Hiroshi Ohta, Miki Nagashima, Satomi Inoue, Ryusuke Nakamura, Naoki Ota, Hideki Maruyama, Tatsuya Gobara, Hideo Endoh, Akira Ando, Masahiko Shiratori, Yoshimune Maruyama, Shoichi Nagoya Univ Grad Sch Med Grad Sch Med 65 Tsurumai ChoShowa Ku Nagoya Aichi 4648550 Japan Nagoya Univ Hosp Dept Adv Med Nagoya Japan Hokkaido Univ Hosp Inst Hlth Sci Innovat Med Care Sapporo Japan Kyusyu Univ Hosp Fukuoka Japan Kyushu Univ Grad Sch Med Sci Dept Med & Clin Sci Fukuoka Japan Okayama Univ Hosp Div Med Informat Okayama Japan Okayama Univ Dept Comprehens Therapy Chron Kidney Dis Fac Med Dent & Pharmaceut Sci Okayama Japan Univ Tokyo Hosp Clin Res Promot Ctr Tokyo Japan Univ Tokyo Hosp Dept Healthcare Informat Management Tokyo Japan Tohoku Univ Hosp Med Informat Technol Ctr Sendai Japan Hokkaido Univ Hosp Dept Med Informat Sapporo Japan Nagoya Univ Hosp Med IT Ctr Nagoya Japan

Background Identifying patients on dialysis among those with an estimated glomerular filtration rate (eGFR) < 15 mL/min/1.73 m(2) remains challenging. To facilitate clinical research in advanced chronic kidney disease (CKD) using electronic health records, we aimed to developa* algorithms to identify dialysis patients using laboratory data obtained in routine practice. Methods We collected clinical data of patients with an eGFR < 15 mL/min/1.73 m2 from six clinical research core hospitals across Japan: four hospitals for the derivation cohort and two for the validation cohort. The candidate factors for the classification models were identified using logistic regression with stepwise backward selection. To ensure transplant patients were not included in the non-dialysis population, we excluded individuals with the disease code Z94.0. Results We collected data from 1142 patients, with 640 (56%) currently undergoing hemodialysis or peritoneal dialysis (PD), including 426 of 763 patients in the derivation cohort and 214 of 379 patients in the validation cohort. The prescription of PD solutions perfectly identified patients undergoing dialysis. After excluding patients prescribed PD solutions, seven laboratory parameters were included in thea* algorithm. The areas under the receiver operation characteristic curve were 0.95 and 0.98 and the positive and negative predictive values were 90.9% and 91.4% in the derivation cohort and 96.2% and 94.6% in the validation cohort, respectively. The calibrations were almost linear. Conclusions We identified patients on dialysis among those with an eGFR < 15 ml/min/1.73 m(2). This study paves the way for database research in nephrology, especially for patients with non-dialysis-dependent advanced CKD.

关键词： Chronic kidney disease algorithm Classification Dialysis

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Development and validation of ana* algorithm to better identify pregnant women with inherited bleeding disorders within electronic health records

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THROMBOSIS RESEARCH 2025年 246卷 109253-109253页

作者： Lim, Ming Y. Sivaloganathan, Vasee Rodgers, George M. Simonsen, Sara E. Univ Utah Div Hematol & Hematol Malignancies Dept Internal Med Salt Lake City UT USA Univ Utah Data Sci Serv Salt Lake City UT USA Univ Utah Coll Nursing Salt Lake City UT USA

Background: When using electronic health records (EHRs) to conduct population-based studies on inherited bleeding disorders (IBDs), using diagnosis codes alone results in a high number of false positive identifications. Objective: The objective of this study was to develop and validate ana* algorithm that uses multiple data elements of EHRs to identify pregnant women with IBDs. Methods: The population included pregnant women who had at least one live birth or fetal death (>20 weeks gestation) at our institution from 2016 to 2023. We iteratively developed thea* algorithm using a composite criteria of encounter diagnosis codes, laboratory and medications data. We assessed the performance of thea* algorithm for sensitivity and positive predictive value (PPV) using our local registry and manual chart review. Results: Using the source population between 2016 and 2020, the initiala* algorithm identified 25 pregnant women with IBDs. Eight women with a known diagnosis of an IBD were missed resulting in a sensitivity of 75.8 % and a PPV of 100 %. We revised thea* algorithm to remove certain IBD diagnosis codes that resulted in contamination and added additional criteria to improve the sensitivity. The reviseda* algorithm had a sensitivity of 97.0 % and a PPV of 91.4 %. The reviseda* algorithm was validated using the source population between 2021 and 2023 and had a sensitivity of 97.1 % and a PPV of 91.7 %. Conclusion: This study demonstrates the utility of ana* algorithm to better identify pregnant women with specific types of IBD, mainly hemophilia and hemophilia carriers, and von Willebrand disease, within EHRs.

关键词： Pregnancy Bleeding disorders Outcomes algorithm

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Evaluating the Impact of a Ki-67 Decision Supporta* algorithm on Pathology Residents' Scoring Accuracy

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JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2025年 1-7页

作者： Aygun, Mine Ilayda Sengor Yalcin, Ozben Uzel, Burak Kulduk, Gamze Comunoglu, Cem Univ Hlth Sci Bagcilar Res & Training Hosp Dept Pathol Istanbul Turkiye Univ Hlth Sci Prof Dr Cemil Tascioglu City Hosp Dept Pathol Istanbul Turkiye Virasoft Co New York NY USA

Ki-67 scoring is of essential importance in the evaluation of breast cancer. We evaluated a Ki-67a* algorithm as a decision support tool to improve accuracy for pathology residents. We retrospectively evaluated Ki-67 scores on whole slide images (WSI) obtained from 156 consecutive breast cancer patients. Two senior pathologists determined the 2.1 mm(2) hotspot to be evaluated. Ki-67 scores from senior pathologists were compared with results generated by thea* algorithm, results from 10 pathology residents, and results from pathology residents with the assistance of thea* algorithm. In addition to numerical results from thea* algorithm, residents were also presented with a visual representation of nuclei that were counted and excluded. Statistical analysis was performed using Wilcoxon and intra-class correlation (ICC) tests. The mean Ki-67 scores from senior pathologists and thea* algorithm were 23 +/- 18 and 24 +/- 18, respectively (ICC, 0.98). Ki-67 scores from the residents were 19 +/- 16 and 22 +/- 16, without and with input from thea* algorithm, respectively. With input from thea* algorithm, residents' scores were significantly closer to those obtained by senior pathologists (p = 0.008). Residents modified their scores in 53.8% of the cases where 74% of the better scores were characterized by an increase in the original scores. The results obtained by the Ki-67a* algorithm were highly correlated with those assessed by senior pathologists. We demonstrated that thea* algorithm may serve as a decision support tool for residents to align their results with those of senior pathologists.

关键词： algorithm Decision support Ki-67 Resident

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Computer Vision and Controla* algorithm for Sustainable and Precision Agriculture

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SN Computer Science 2025年第4期6卷 1-13页

作者： Adebayo, Segun Aworinde, Halleluyah O. Ojerinde, Stephen A. Oyedoja, Kayode O. Adeniyi, Abidemi Emmanuel Olanloye, Odunayo Dauda Osueke, Christian O. Ogundokun, Roseline Oluwaseun Das, Abhishek Mechatronics Engineering Bowen University Iwo Nigeria Computer Science Bowen University Iwo Nigeria Computer Science Western Illinois University IL United States Technology Education Emmanuel Alayande University of Education Oyo Nigeria Department of Software Engineering Kaunas University of Technology Kaunas Lithuania Department of Computer Science Landmark University Omu-Aran Kwara State Nigeria Department of Computer Science and Engineering Aliah University Kolkata India Tshwane University of Technology Pretoria South Africa Durban University of Technology Durban South Africa Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

The world population continues to increase with a large proportion of this increase happening in Africa just as predicated by Food and Agriculture Organization of the United Nation. To meet the demand of feeding this increasing population, innovative farming practices are being developed to increase the yield of the major stable food supply. This research developed a computer vision and controla* algorithm on a quadcopter for farm surveillance, crop protection from the invasion of pest, early disease detection as well as crop yield estimation. A process of developing a model capable of detecting and classifying these pests in the environment was presented in this study. The process involves selecting, preprocess and transforming the sensed data from a vision sensor to train classificationa* algorithm and to detect and track object of interest in the environment. This study addresses a notable research gap in computer vision, control systems, big data analytics, and robotics, specifically within the context of smart farming. It primarily utilizes conventional learninga* algorithms, including Artificial Neural Networks and Support Vector Machines, typically found in widely-used commercial software. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： algorithm Computer vision Machine learning Quadcopter UAV

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Anthropomorphising thea* algorithm: A 'Theory of Mind' Perspective on Psychological Contract Creation in Gig Work Arrangements

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HUMAN RESOURCE MANAGEMENT JOURNAL 2025年第0期

作者： Sherman, Ultan Rousseau, Denise M. Carbery, Ronan Mcdonnell, Anthony Duggan, James Morley, Michael J. Cork Univ Business Sch Univ Coll Cork Cork Ireland Carnegie Mellon Univ Heinz Coll Pittsburgh PA USA Univ Limerick Kemmy Business Sch Limerick Ireland

algorithms increasingly manage the exchange between workers and organisations. This raises the question of whether agency can be attributed to, and psychological contracts formed with, such non-human organisational representatives. Against this backdrop, we assemble and critically review streams of literature regarding app working and psychological contract making to better understand the psychological dynamics of app-work. We show how such non-human entities are commonly ascribed traits and intentions characteristic of a human agent, which foster positive emotions and relationship continuity for app-workers. To theoretically explain the mechanisms at play in how app-workers may create a psychological contract with ana* algorithm, we curate ideas from ‘Theory of Mind’ which accounts for key aspects of relationship development through direct experience with a target party. Our work unpacks the experiences of app-workers and underscores the expanding role of the HR function in managing such independent contractors, along with the evolving means by which this can be achieved. Arising from our review, we set down key implications for the role ofa* algorithmic agents in the design of work and the management practice of HR professionals.

关键词： agency algorithm anthropomorphism gig work psychological contract theory of mind

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Novel automated electronic medical record-based VEST (virtual echocardiography screening tool)a* algorithm for pulmonary arterial hypertension

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American Heart Journal 2025年 287卷 24-31页

作者： Anand, Suneesh Chembiparambil Narowska, Gabriela Gangireddy, Chethan Enevoldsen, John Keane, Martin Gerard Edmundowicz, Daniel Forfia, Paul Robert Vaidya, Anjali Division of Cardiology Advanced Pulmonary Hypertension Right Heart Failure CTEPH Program Temple University Lewis Katz School of Medicine & Temple University Hospital Philadelphia PA United States Department of Medicine Temple University Lewis Katz School of Medicine & Temple University Hospital Philadelphia PA United States Medical Informatics Temple University Lewis Katz School of Medicine & Temple University Hospital Philadelphia PA United States Division of Cardiology Temple University Lewis Katz School of Medicine & Temple University Hospital Philadelphia PA United States

Background: Pulmonary arterial hypertension (PAH) remains underrecognized and life-threatening due to limited awareness, nonspecific symptoms, and late referral to accredited pulmonary hypertension (PH) centers. The previously validated virtual echocardiography screening tool (VEST) predicts PAH hemodynamics. The objectives of the present study were to determine if the novel automated electronic medical record (EMR)-baseda* algorithm could accurately calculate VEST scores to identify PAH hemodynamics and aid referral to PH specialty care. Methods: This study is a retrospective analysis of 4,952 patients who underwent transthoracic echocardiogram (TTE) with tricuspid regurgitation velocity (TRV) ≥2.9 m/s in a hospital with an accredited PH Center of Comprehensive Care. Using the automated EMR-baseda* algorithm, EMR-calculated VEST scores were calculated and compared to manually calculated VEST scores. Automated EMR VEST scores were used to identify those with highest risk for PAH (+3 score). Patients with +3 score were analyzed to determine whether they were evaluated within the accredited PH center or undergone right heart catheterization (RHC), the gold standard for PH diagnosis. Results: Automated EMR VEST scores were validated with 100% correlation to 60 manual scores. Of 354 patients with +3 score, those that underwent RHC had severe PH, with mean pulmonary artery pressure 48 mm Hg and pulmonary vascular resistance 8.5 Wood units. One hundred and four patients (29.4%) were never referred for specialty PH care, and of these, only 37.5% underwent RHC. In the 250 patients referred to subspecialty PH care, 237 (94.8%) underwent RHC. Conclusions: This novel EMR-based automated VEST calculator is a powerful yet simple scoring tool that can capture patients at high risk for PAH, prompting earlier diagnosis and referrals to accredited PH centers to allow for earlier expert care and implementation of medical therapies. © 2025 The Authors

关键词： algorithm Echocardiogram Electronic decision support Electronic health record Electronic medical record Hemodynamics Pulmonary arterial hypertension Pulmonary hypertension Right heart catheterization Risk prediction score

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