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1
Koren, Yoram. "Control of Machine Tools." Journal of Manufacturing Science and Engineering 119, no. 4B (November 1, 1997): 749–55. http://dx.doi.org/10.1115/1.2836820.
Анотація:
This paper reviews the progress in machine tool control during the last three decades. Three types of controls are discussed: (i) Servocontrol loops that control the individual axes of the machine, (ii) interpolators that coordinate the motion of several axes, and (iii) adaptive control that adjusts the cutting variables in real time to maximize system performance. We cover a selection of the most advanced techniques utilized in each of these types, and draw conclusions based on experimental results.
2
Chen, Mingzhang, Xinfei Ning, Zijian Zhou, Yuwen Shu, Yun Tang, Yang Cao, Xuebing Shang, and Xinghui Han. "LMS/RLS/OCTAVE Vibration Controls of Cold Orbital Forging Machines for Improving Quality of Forged Vehicle Parts." World Electric Vehicle Journal 13, no. 5 (April 27, 2022): 76. http://dx.doi.org/10.3390/wevj13050076.
Анотація:
Cold orbital forging (COF) as an advanced incremental metal-forming technology has been widely used in processing vehicle parts. During the COF process, the vibration on the COF machine injures the service life of the machine and the quality of the forged part. The study of the vibration control of the COF machine is therefore necessary. In this study, the dynamic model of the COF machine is established, and the vibration performances of some key positions are obtained using Matlab&Simulink software. Subsequently, the vibration performances are effectively verified by conducting a vibration test experiment. Based on the dynamics model of the COF machine and Matlab&Simulink software, least-mean-squares (LMS), recursive least-squares (RLS) and OCTAVE vibration-control algorithms are applied to reduce the vibration. Comparing the vibration performances of the COF machine, these vibration-control algorithms are useful for reducing the vibration of the machine, which improves the service life of the machine and the quality of the forged part. Based on the vibration performances of the COF machine, the effects of LMS and RLS vibration controls are better than the OCTAVE, and they also obviously reduce the vibration of the COF machine. The vibration-control algorithms are first to be applied to reduce the vibration of the COF machines in this study, which will be beneficial to future research on the vibration controls of metal-forming machines and other mechanical systems.
3
Wright, Alan D., and Mark J. Balas. "Design of Controls to Attenuate Loads in the Controls Advanced Research Turbine." Journal of Solar Energy Engineering 126, no. 4 (November 1, 2004): 1083–91. http://dx.doi.org/10.1115/1.1792654.
Анотація:
The wind industry seeks to design wind turbines to maximize energy production and increase fatigue life. To achieve this goal, we must design wind turbines to extract maximum energy and reduce component and system loads. This paper applies modern state-space control design methods to a two-bladed teetering-hub upwind machine located at the National Wind Technology Center. The design objective is to regulate turbine speed in region 3 (above rated wind speed) and enhance damping in several low-damped flexible modes of the turbine. The controls approach is based on the Disturbance Accommodating Control method and provides accountability for wind-speed disturbances. First, controls are designed with the single control input rotor collective pitch to stabilize the first drive-train torsion as well as the tower first fore-aft bending modes. Generator torque is then incorporated as an additional control input. This reduces some of the demand placed on the rotor collective pitch control system and enhances first drive train torsion mode damping. Individual blade pitch control is then used to attenuate wind disturbances having spatial variation over the rotor and effectively reduces blade flap deflections caused by wind shear.
4
Kim, Joong Nam. "Man-Machine Interface Design for Korean Next Generation Reactor: A Human Factors Perspective." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 44, no. 22 (July 2000): 823–26. http://dx.doi.org/10.1177/154193120004402285.
Анотація:
The concept of advanced man-machine interface (MMI) technology is employed to the design of the main control room (MCR) for Korean Next Generation Reactor (KNGR), an advanced light water nuclear power plant (NPP) currently under development. In the KNGR MCR, computerized workstations and digital operating systems provide the operator with electronically generated graphics and information for system status displays and plant controls. The introduction of digital technology to the development of advanced power plant control station has brought new issues and concerns associated with the KNGR MMI design, especially in human factors perspective. This paper presents some of human factors efforts in the development of KNGR MMIs that includes large screen display, workstation CRT, computerized procedure, and soft controller.
5
Xavier, André Amorim Gonçalves, Flavio Maldonado Bentes, Marcelo de Jesus Rodrigues da Nóbrega, Fabiano Battemarco da Silva Martins, and Hildson Rodrigues de Queiroz. "CNC Machine Building Through Open Sources Projects and Programs." International Journal for Innovation Education and Research 8, no. 9 (September 1, 2020): 108–18. http://dx.doi.org/10.31686/ijier.vol8.iss9.2600.
Анотація:
This research presents a theoretical and practical approach on the construction of a low-cost CNC machine, using as a base model found on the internet. Having his knowledge of movement that in the past was movement only one axis at a time, and in current controls movement on three simultaneous axes. The use of an Arduino micro controller will be the key part of the machine's operation, since it behaves like a PLC, starting from basic to advanced programming. The use of the project will end in its academic use and the dissemination of the technology used
6
Maleki, Ehsan, Brice Pridgen, William Singhose, Urs Glauser, and Warren Seering. "Educational Use of a Small-Scale Cherrypicker." International Journal of Mechanical Engineering Education 40, no. 2 (April 2012): 104–20. http://dx.doi.org/10.7227/ijmee.40.2.2.
Анотація:
Cherrypickers are a useful class of machines that can lift people to great heights. Unfortunately, the operator bucket oscillates and the entire machine can tip over in catastrophic accidents. Understanding the dynamics and stability of these machines is crucial for efficient and safe operation. To this end, a small-scale cherrypicker was constructed for experimental dynamic analysis and educational use. The dynamic behavior of the cherrypicker, as well as the improved system response using vibration-control techniques are presented. The cherrypicker was used during fall 2010 as an experimental apparatus in an advanced graduate controls course taught simultaneously at the Georgia Institute of Technology and the Massachusetts Institute of Technology. Its educational use in this multi-institutional course is discussed.
7
O’Brien, Megan K., Olivia K. Botonis, Elissa Larkin, Julia Carpenter, Bonnie Martin-Harris, Rachel Maronati, KunHyuck Lee, et al. "Advanced Machine Learning Tools to Monitor Biomarkers of Dysphagia: A Wearable Sensor Proof-of-Concept Study." Digital Biomarkers 5, no. 2 (July 27, 2021): 167–75. http://dx.doi.org/10.1159/000517144.
Анотація:
<b><i>Introduction:</i></b> Difficulty swallowing (dysphagia) occurs frequently in patients with neurological disorders and can lead to aspiration, choking, and malnutrition. Dysphagia is typically diagnosed using costly, invasive imaging procedures or subjective, qualitative bedside examinations. Wearable sensors are a promising alternative to noninvasively and objectively measure physiological signals relevant to swallowing. An ongoing challenge with this approach is consolidating these complex signals into sensitive, clinically meaningful metrics of swallowing performance. To address this gap, we propose 2 novel, digital monitoring tools to evaluate swallows using wearable sensor data and machine learning. <b><i>Methods:</i></b> Biometric swallowing and respiration signals from wearable, mechano-acoustic sensors were compared between patients with poststroke dysphagia and nondysphagic controls while swallowing foods and liquids of different consistencies, in accordance with the Mann Assessment of Swallowing Ability (MASA). Two machine learning approaches were developed to (1) classify the severity of impairment for each swallow, with model confidence ratings for transparent clinical decision support, and (2) compute a similarity measure of each swallow to nondysphagic performance. Task-specific models were trained using swallow kinematics and respiratory features from 505 swallows (321 from patients and 184 from controls). <b><i>Results:</i></b> These models provide sensitive metrics to gauge impairment on a per-swallow basis. Both approaches demonstrate intrasubject swallow variability and patient-specific changes which were not captured by the MASA alone. Sensor measures encoding respiratory-swallow coordination were important features relating to dysphagia presence and severity. Puree swallows exhibited greater differences from controls than saliva swallows or liquid sips (<i>p</i> < 0.037). <b><i>Discussion:</i></b> Developing interpretable tools is critical to optimize the clinical utility of novel, sensor-based measurement techniques. The proof-of-concept models proposed here provide concrete, communicable evidence to track dysphagia recovery over time. With refined training schemes and real-world validation, these tools can be deployed to automatically measure and monitor swallowing in the clinic and community for patients across the impairment spectrum.
8
Purushotham, Dr M. "Advanced Key Foundations of Multiagent System." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (March 31, 2023): 1–6. http://dx.doi.org/10.22214/ijraset.2023.49153.
Анотація:
Abstract: Ethics is inherently a multiagent concern. However, analysis on AI ethics nowadays is dominated by work on individual agents: (1) however Associate in Nursing autonomous golem or automotive could hurt or (differentially) profit folks in theoretical things (the questionable tramcar problems) and (2) how a machine learning formula could turn out biased choices or recommendations. The social group framework is basically omitted. To develop new foundations for ethics in AI, we tend to adopt a sociotechnical stance during which agents (as technical entities) facilitate autonomous social entities or principals (people and organizations). This multiagent conception of a sociotechnical system (STS) captures however moral considerations arise within the mutual interactions of multiple stakeholders. These foundations would modify USA to understand ethical STSs that incorporate social and technical controls to respect stated moral postures of the agents within the STSs. The visualized foundations need new thinking, on 2 broad themes, on how to realize (1) Associate in STS that reflects its stakeholders’ values and (2) individual agents that perform effectively in such Associate in STS.
9
Hafiz, Mohd Zani, Halim Isa, and Muhammad Syafiq Syed Mohamed. "An Overview of Ergonomics Problems Related to CNC Machining Operations." Advanced Engineering Forum 10 (December 2013): 137–42. http://dx.doi.org/10.4028/www.scientific.net/aef.10.137.
Анотація:
In the new era of advanced manufacturing technology, machine tool design plays an important role in maximizing productivity and occupational health of industrial workers. However, the machine tool manufacturers face difficulty in designing an ergonomic machine tool that can be suited to Malaysian industrial workers because almost all machine tools were designed according to physical dimensions, capabilities and limitations of European or American populations. This mismatch between machine design and worker abilities may eventually lead to occupational injuries. Therefore, the purpose of this paper is to disseminate information on ergonomics problem, assessment methods, and control measures associated with CNC machining operation. Published articles related to CNC machining operation have been reviewed. Based on published researches, work-related musculoskeletal disorders such as low-back pain, neck and shoulder problem have been identified as common health problems associated with the machine operation. Engineering and administrative controls have been proposed to minimize the health problems.
10
Castro-Martin, Ana Pamela, Horacio Ahuett-Garza, Darío Guamán-Lozada, Maria F. Márquez-Alderete, Pedro D. Urbina Coronado, Pedro A. Orta Castañon, Thomas R. Kurfess, and Emilio González de Castilla. "Connectivity as a Design Feature for Industry 4.0 Production Equipment: Application for the Development of an In-Line Metrology System." Applied Sciences 11, no. 3 (February 1, 2021): 1312. http://dx.doi.org/10.3390/app11031312.
Анотація:
Industry 4.0 (I4.0) is built upon the capabilities of Internet of Things technologies that facilitate the recollection and processing of data. Originally conceived to improve the performance of manufacturing facilities, the field of application for I4.0 has expanded to reach most industrial sectors. To make the best use of the capabilities of I4.0, machine architectures and design paradigms have had to evolve. This is particularly important as the development of certain advanced manufacturing technologies has been passed from large companies to their subsidiaries and suppliers from around the world. This work discusses how design methodologies, such as those based on functional analysis, can incorporate new functions to enhance the architecture of machines. In particular, the article discusses how connectivity facilitates the development of smart manufacturing capabilities through the incorporation of I4.0 principles and resources that in turn improve the computing capacity available to machine controls and edge devices. These concepts are applied to the development of an in-line metrology station for automotive components. The impact on the design of the machine, particularly on the conception of the control, is analyzed. The resulting machine architecture allows for measurement of critical features of all parts as they are processed at the manufacturing floor, a critical operation in smart factories. Finally, this article discusses how the I4.0 infrastructure can be used to collect and process data to obtain useful information about the process.
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Perez-Gracia, Jose Luis, Elisabet Guruceaga, Maria Pilar Andueza, Marimar Ocon, Nicolas de VIllalonga Zornoza, Jafait Junior Fodop Sokoudjou, Gorka Alkorta-Aranburu, et al. "Whole exome sequencing and machine learning germline analysis of individuals presenting with phenotypes of extreme high and low risk of developing tobacco-induced lung adenocarcinoma." Journal of Clinical Oncology 41, no. 16_suppl (June 1, 2023): 10507. http://dx.doi.org/10.1200/jco.2023.41.16_suppl.10507.
Анотація:
10507 Background: Tobacco is the main risk factor for developing lung cancer. Yet, while some heavy smokers develop lung cancer at young age others never develop it, even at advanced age. This suggests a remarkable variability in the individual susceptibility to the carcinogenic effects of tobacco. We characterized the germline profile of subjects presenting these extreme phenotypes with Whole Exome Sequencing (WES) and Machine Learning (ML). Methods: We sequenced germline DNA from heavy smokers who either developed lung adenocarcinoma at early age ( extreme cases) or did not develop it at advanced age ( extreme controls). The discovery and validation cohorts included respectively 50 and 66 extreme cases and 50 and 83 extreme controls, selected from databases including > 6,000 subjects. We selected individual coding variants and variant-rich genes showing a significantly different distribution between extreme cases and controls. We trained ML models (Logistic Regression, Random Forest, Support Vector machine Classifier (SVC)) on the discovery cohort to classify subjects into their respective phenotypes and tested them on the validation cohort. Results: Mean age for extreme cases and controls in both cohorts was 50.2 and 78.4 years. Mean tobacco consumption was 38.1 and 59.1 pack-years. We validated 16 significant individual variants. The most significant variants were in ADAMTS7 (2 variants) in cases and TMEM191B (1) in controls. We validated 33 genes enriched with significant variants. The genes harboring more variants were HLA-A (4 variants) and ADAMTS7 (2) in cases; and PLIN4 (2) in controls (Table). We trained several ML models on the discovery cohort using as input the 16 significant individual variants and the number of variants in the 33 enriched genes. We tested them in the validation cohort obtaining accuracy of 72% and AUC-ROC of 87.4% with the best model (SVC), using 16 variants as input, confirming their association with the phenotypes. Functions of validated genes included oncogenes, tumor-suppressors, DNA repair, maintenance of genomic stability, HLA mediated antigen presentation and regulation of proliferation, migration, apoptosis and inflammatory pathways. Conclusions: Individuals presenting phenotypes of extreme high and low risk of developing tobacco-induced lung adenocarcinoma have different germline profiles. Our strategy may allow to identify high-risk subjects and to develop new therapeutic approaches. [Table: see text]
12
Chi, Stephen, Seunghwan Kim, Matthew Reuter, Katharine Ponzillo, Debra Parker Oliver, Randi Foraker, Kevin Heard, et al. "Advanced Care Planning for Hospitalized Patients Following Clinician Notification of Patient Mortality by a Machine Learning Algorithm." JAMA Network Open 6, no. 4 (April 18, 2023): e238795. http://dx.doi.org/10.1001/jamanetworkopen.2023.8795.
Анотація:
ImportanceGoal-concordant care is an ongoing challenge in hospital settings. Identification of high mortality risk within 30 days may call attention to the need to have serious illness conversations, including the documentation of patient goals of care.ObjectiveTo examine goals of care discussions (GOCDs) in a community hospital setting with patients identified as having a high risk of mortality by a machine learning mortality prediction algorithm.Design, Setting, and ParticipantsThis cohort study took place at community hospitals within 1 health care system. Participants included adult patients with a high risk of 30-day mortality who were admitted to 1 of 4 hospitals between January 2 and July 15, 2021. Patient encounters of inpatients in the intervention hospital where physicians were notified of the computed high risk mortality score were compared with patient encounters of inpatients in 3 community hospitals without the intervention (ie, matched control).InterventionPhysicians of patients with a high risk of mortality within 30 days received notification and were encouraged to arrange for GOCDs.Main Outcomes and MeasuresThe primary outcome was the percentage change of documented GOCDs prior to discharge. Propensity-score matching was completed on a preintervention and postintervention period using age, sex, race, COVID-19 status, and machine learning-predicted mortality risk scores. A difference-in-difference analysis validated the results.ResultsOverall, 537 patients were included in this study with 201 in the preintervention period (94 in the intervention group; 104 in the control group) and 336 patients in the postintervention period. The intervention and control groups included 168 patients per group and were well-balanced in age (mean [SD], 79.3 [9.60] vs 79.6 [9.21] years; standardized mean difference [SMD], 0.03), sex (female, 85 [51%] vs 85 [51%]; SMD, 0), race (White patients, 145 [86%] vs 144 [86%]; SMD 0.006), and Charlson comorbidities (median [range], 8.00 [2.00-15.0] vs 9.00 [2.00 to 19.0]; SMD, 0.34). Patients in the intervention group from preintervention to postintervention period were associated with being 5 times more likely to have documented GOCDs (OR, 5.11 [95% CI, 1.93 to 13.42]; P = .001) by discharge compared with matched controls, and GOCD occurred significantly earlier in the hospitalization in the intervention patients as compared with matched controls (median, 4 [95% CI, 3 to 6] days vs 16 [95% CI, 15 to not applicable] days; P &lt; .001). Similar findings were observed for Black patient and White patient subgroups.Conclusions and RelevanceIn this cohort study, patients whose physicians had knowledge of high-risk predictions from machine learning mortality algorithms were associated with being 5 times more likely to have documented GOCDs than matched controls. Additional external validation is needed to determine if similar interventions would be helpful at other institutions.
13
B N, Rekha. "Gesture Controlled Virtual Mouse using AI." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 2411–16. http://dx.doi.org/10.22214/ijraset.2023.52100.
Анотація:
Abstract: This project offers a cursor controlsystem that quickly navigates system controls whileusing a voice assistant and a camera to record usermotions. Using the aid of MediaPipe, the user can control the computer cursor with hand gestures. It will perform actions like left clicking and draggingusing a variety of hand motions. Additionally, you have a choice to adjust the brightness, loudness, and a number of other things. The system is constructed using advanced Python packages like MediaPipe, OpenCV, etc. All i/o activities are physically controlled by a hand motion and a voiceassistance. The research uses advanced technologies like machine learning and computer vision techniques, which operates well without the use of any additional computer resources, to recognize hand movements and spoken instructions.
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Rana, Md Shohel, and Andrew H. Sung. "Evaluation of Advanced Ensemble Learning Techniques for Android Malware Detection." Vietnam Journal of Computer Science 07, no. 02 (February 20, 2020): 145–59. http://dx.doi.org/10.1142/s2196888820500086.
Анотація:
Android is the most well-known portable working framework having billions of dynamic clients worldwide that pulled in promoters, programmers, and cybercriminals to create malware for different purposes. As of late, wide-running inquiries have been led on malware examination and identification for Android gadgets while Android has likewise actualized different security controls to manage the malware issues, including a User ID (UID) for every application, framework authorizations. In this paper, we advance and assess various kinds of machine learning (ML) by applying ensemble-based learning systems for identifying Android malware related to a substring-based feature selection (SBFS) strategy for the classifiers. In the investigation, we have broadened our previous work where it has been seen that the ensemble-based learning techniques acquire preferred outcome over the recently revealed outcome by directing the DREBIN dataset, and in this manner they give a solid premise to building compelling instruments for Android malware detection.
15
Desai, Khushbu, Alan Mitchell, Ankita Shah, Dharini Chandrasekar, Gege Xu, Klaus Lindpaintner, Dan Serie, Tillman E. Pearce, and Daniel Hommes. "Use of glycoproteome profiles to detect advanced adenomas and colorectal cancer." Journal of Clinical Oncology 41, no. 4_suppl (February 1, 2023): 69. http://dx.doi.org/10.1200/jco.2023.41.4_suppl.69.
Анотація:
69 Background: Colorectal cancer (CRC) remains a leading cancer despite current screening modalities. Precancerous lesions, or Advanced Adenomas (AA), commonly precede invasive cancer development by years. Newer technologies use circulating tumor DNA and/or proteins for CRC detection but have not been able to effectively detect AA. Aberrant protein glycosylation is associated with (pre-)malignant lesions. To detect glycoproteome profiles associated with the occurrence of AA, we studied serum glycoproteins in AA/CRC. Methods: A novel platform combining liquid-chromatography/mass-spectrometry (LC-MS) and artificial-intelligence (AI)-powered data processing allowing high resolution, high throughput glycoproteomic profiling was used to identify glycoprotein biomarkers in peripheral blood. Samples were sourced from biorepositories and included patients diagnosed with CRC, AA, ulcerative colitis (UC) and controls. The samples were split into a training (50%) and a hold-out testing set (50%) for the development of a machine learning (ML)-based multivariable predictive model. Statistical analysis was performed on normalized data to identify biomarkers differentiating AAs and different stages of CRC from controls. Results: We studied 563 patient samples: 196 controls (mean age 51.7; 52% female); 32 AA (mean age 68.6; 53% female); 247 CRC (mean age 65.6; 50% female) and 88 UC (mean age 44.1; 47% female). There were 250 differentially abundant (FDR < 0.05) glycopeptides/peptides when comparing CRC and AA samples with healthy and UC controls. A subset was assessed, generating a six (6) biomarker ML classification model. This model was applied to the hold-out test and achieved an overall sensitivity of 91.4% and specificity of 91.8% for predicting AA/CRC versus healthy/UC with an area under the receiver operating characteristic of 0.962. AA and CRC separately were predicted with a sensitivity of 84.4% and 92.8%, respectively, relative to healthy/UC with sensitivities for CRC stage 1/2 and stage 3/4 being 91.2% and 93.2%, respectively). Conclusions: Glycoproteomic serum profiles accurately detect precancerous AA in addition to CRC and offer a new approach to effective CRC screening. We will have completed an interim analysis of a large prospective observational study at the time of the meeting. Clinical trial information: NCT05445570 . [Table: see text]
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Oluwole-ojo, Oluwaloba, Hongwei Zhang, Martin Howarth, and Xu Xu. "Energy Consumption Analysis of a Continuous Flow Ohmic Heater with Advanced Process Controls." Energies 16, no. 2 (January 12, 2023): 868. http://dx.doi.org/10.3390/en16020868.
Анотація:
This paper presents an analysis of the energy consumption of a continuous flow ohmic heater (CFOH) with advanced process controls for heating operations in the food and drinks industry. The study was carried out by using operational data collected from a CFOH pilot plant that was designed and constructed at the National Centre of Excellence for Food Engineering (NCEFE), Sheffield Hallam University. The CFOH is controlled by a PC and includes an onboard Programmable Logic Controller (PLC) and a Human Machine Interface (HMI) so that it can be operated as a stand-alone unit with basic on/off and power setting control but without any advanced control features. The technical solution presented in this paper for heating foods demonstrates significant energy saving compared with conventional heating methods. Using the CFOH, the electric current generated in the food products by the Joule effect produces a rapid temperature increase with very high energy efficiency. This technique eliminates the low efficiency of heat transfer from the surface of vessels typically used to heat and cook food products. The analysis presented in this paper describes the energy consumption of the CFOH and compares the efficiency of the CFOH when different advanced process control techniques are used. Experimental results and analysis have shown that the CFOH can achieve an energy efficiency conversion of at least 87.9%. It has also shown that the energy conversion percentage can be increased by applying advanced controllers such as model predictive control (MPC) or adaptive model predictive control (AMPC).
17
Ehrensberger, Sahar Hosseinian, Victoria Wosika, Noushin Hadadi, Sara Fonseca Costa, Eric Durandau, Sylvain Monnier-Benoit, Laura Ciarloni, Stefan J. Scherer, and Sabine Tejpar. "Next-generation whole blood transcriptome-based assay for advanced adenoma detection." Journal of Clinical Oncology 41, no. 4_suppl (February 1, 2023): 77. http://dx.doi.org/10.1200/jco.2023.41.4_suppl.77.
Анотація:
77 Background: Colorectal Cancer (CRC) is the second leading cause of cancer mortality worldwide although highly curable if detected early. An accurate liquid biopsy test for early cancer and precancerous lesion, detection is key to promote prevention and reduce mortality. We previously developed a liquid biopsy test based on immune cells response to the tumor, commercialised under the name of Colox, with best-in-class clinical performances for AA detection (52% sensitivity and 92% specificity) (Ciarloni L et al, Clin Cancer Res, 2016). An improved second generation of the test, leveraging transcriptome profiling and advanced machine learning tools, is under development and first results are presented. Methods: Prospective peripheral whole-blood (PAXgene) samples from subjects diagnosed with CRC, advanced adenoma (AA), non-advanced adenoma, other types of cancer as well as controls without colorectal neoplasia (CON) were divided into discovery and validation sets. Transcriptome profiles were generated by RNA-seq and gene expression signatures identified leveraging Novigenix’s proprietary LITOseek platform, which integrates several advanced Machine Learning (ML) methods into a ranking systems. Biological functional analysis was performed by over-representation (ORA), gene set enrichment (GSEA) and gene network analyses (STRINGdb). Results: Significant changes in the whole blood transcriptome profile of AA compared to CON were identified. Functional analysis highlighted upregulation of fatty acid derivative biosynthesis, interferon signaling, tryptophan catabolism to kynurenine, and downregulation of DNA repair in AA blood samples compared to CON. A novel gene classifier was generated, showing for AA detection 71% sensitivity, 94% specificity and an AUC of 74% by cross-validation on the discovery set. Validation of the gene classifier in an independent set is currently ongoing and will be presented at the congress. Conclusions: Capturing immune-related information using whole blood trascriptome profiling and applying cutting-edge machine learning technologies demonstrated to be valuable for identifying gene signatures for advanced adenoma detection. This differentiated solution has demonstrated best-in-class potential to significantly improve patient outcomes by detecting precancerous lesions with a simple non-invasive blood test.
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Xu, Yaochen, Qinglan Ma, Jingxin Ren, Lei Chen, Wei Guo, Kaiyan Feng, Zhenbing Zeng, Tao Huang, and Yudong Cai. "Using Machine Learning Methods in Identifying Genes Associated with COVID-19 in Cardiomyocytes and Cardiac Vascular Endothelial Cells." Life 13, no. 4 (April 14, 2023): 1011. http://dx.doi.org/10.3390/life13041011.
Анотація:
Corona Virus Disease 2019 (COVID-19) not only causes respiratory system damage, but also imposes strain on the cardiovascular system. Vascular endothelial cells and cardiomyocytes play an important role in cardiac function. The aberrant expression of genes in vascular endothelial cells and cardiomyocytes can lead to cardiovascular diseases. In this study, we sought to explain the influence of respiratory syndrome coronavirus 2 (SARS-CoV-2) infection on the gene expression levels of vascular endothelial cells and cardiomyocytes. We designed an advanced machine learning-based workflow to analyze the gene expression profile data of vascular endothelial cells and cardiomyocytes from patients with COVID-19 and healthy controls. An incremental feature selection method with a decision tree was used in building efficient classifiers and summarizing quantitative classification genes and rules. Some key genes, such as MALAT1, MT-CO1, and CD36, were extracted, which exert important effects on cardiac function, from the gene expression matrix of 104,182 cardiomyocytes, including 12,007 cells from patients with COVID-19 and 92,175 cells from healthy controls, and 22,438 vascular endothelial cells, including 10,812 cells from patients with COVID-19 and 11,626 cells from healthy controls. The findings reported in this study may provide insights into the effect of COVID-19 on cardiac cells and further explain the pathogenesis of COVID-19, and they may facilitate the identification of potential therapeutic targets.
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Sonawane, Ms Priti V., Ms Pooja B. Kamble, Ms Prajakta P. Lohar, Ms Sakshi D. Shete, and Ms Sanika S. Shinde. "Parkinson’s Disease Detection." International Journal for Research in Applied Science and Engineering Technology 12, no. 3 (March 31, 2024): 1388–91. http://dx.doi.org/10.22214/ijraset.2024.59073.
Анотація:
Abstract: Parkinson’s Disease is a disorder that affects the nervous system. Parkinson’s disease does not directly cause people to die but can make some people more vulnerable to serious and life-threatening infections. This research addresses the limitations of traditional clinical diagnosis by harnessing the potential of advanced data analysis techniques and machine learning algorithms. The project’s primary objectives include dataset compilation, feature extraction, model development, multimodal fusion, model validation, and considerations for clinical applicability. The dataset will encompass a diverse range of participants diagnosed with PD as well as healthy controls, ensuring the representation of various demographic and clinical factors. By extracting distinctive features from voice recordings, handwriting dynamics, and gait patterns, the project aims to capture unique biomarkers associated with PD. Machine learning models, tailored for each modality, will be developed to classify individuals as PD-positive or PD-negative
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Abha Mahalwar, Abha, and Rishabh Sharma. "Cyber-Physical Systems: Challenges and Future Directions." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 11, no. 3 (December 15, 2020): 2865–70. http://dx.doi.org/10.61841/turcomat.v11i3.14651.
Анотація:
Cyber-Physical Systems (CPS) integrate computational algorithms with physical components, enabling advanced functionalities in various domains. This paper explores the challenges and future directions of CPS, focusing on security, safety, privacy, and interoperability. In terms of security, CPS face threats to confidentiality, integrity, and availability, necessitating advancements in intrusion detection, prevention systems, and secure communication protocols. Safety improvements include predictive maintenance and autonomous decision-making systems to enhance reliability and resilience. Privacy-enhancing techniques like anonymization and user-centric controls are crucial for data protection. Interoperability solutions, such as middleware and semantic frameworks, facilitate seamless integration among heterogeneous CPS components. Future directions involve leveraging machine learning and AI for security, integrating digital twins for predictive maintenance, and enhancing user-centric privacy controls. These advancements are vital for the continued development and adoption of CPS in diverse applications.
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Moulichon, Audrey, Mazen Alamir, Vincent Debusschere, Lauric Garbuio, and Nouredine Hadjsaid. "Polymorphic Virtual Synchronous Generator: An Advanced Controller for Smart Inverters." Energies 16, no. 20 (October 13, 2023): 7075. http://dx.doi.org/10.3390/en16207075.
Анотація:
Virtual synchronous generators (VSGs) are one of the most relevant solutions to integrate renewable energy in weak grids and microgrids. They indeed provide inverters characteristics of rotating machines (inertia for instance) that are useful for stabilizing the system, notably in the context of the high variability of the production. Thanks to the virtual characteristics of the VSG, the virtual parameters of the emulated synchronous machine can be optimally adapted online as a function of the electric environment of the inverter. We call that inverter’s control a polymorphic VSG. The online adaptation of the critical control parameters of the VSG helps reduce the risk of deterioration of the inverter’s constituents that might be induced by harsh events (frequent in weak grids) but, more importantly, improves the robustness of the system. In this paper, four implementations of a polymorphic VSG controller are compared on a simple microgrid study case to a complete VSG model. For the test, polymorphic VSGs have to minimize frequency and voltage oscillations while withstanding short circuits, which is typically a requirement for units in this context. One of the controls is based on recurrent optimization over a prediction time horizon, and two sub-optimal ones target practical implementation in industrial inverters with limited computational power. Results show a clear reduction in incidents in the microgrid thanks to the controllers. The error reduction with the complete polymorphic VSG is up to 100% for the voltage, 32% for the currents, and 79% for the duty ratio. Those values are decreased by 30 to 50% with the sub-optimal controllers but for a reduction in the computational burden of more than 97%. Recommendations are proposed for the development of an auto-adaptive polymorphic VSG from a high technology-readiness-level perspective, i.e., targeting a compromise between error reduction and computational burden.
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Lopez, Pedro, Ignacio Reyes, Nathalie Risso, Moe Momayez, and Jinhong Zhang. "Machine Learning Algorithms for Semi-Autogenous Grinding Mill Operational Regions’ Identification." Minerals 13, no. 11 (October 25, 2023): 1360. http://dx.doi.org/10.3390/min13111360.
Анотація:
Energy consumption represents a significant operating expense in the mining and minerals industry. Grinding accounts for more than half of the mining sector’s total energy usage, where the semi-autogenous grinding (SAG) circuits are one of the main components. The implementation of control and automation strategies that can achieve production objectives along with energy efficiency is a common goal in concentrator plants. However, designing such controls requires a proper understanding of process dynamics, which are highly complex, coupled, and have non-deterministic components. This complex and non-deterministic nature makes it difficult maintain a set-point for control purposes, and hence operations focus on an optimal control region, which is defined in terms of desirable behavior. This paper investigates the feasibility of employing machine learning models to delineate distinct operational regions within in an SAG mill that can be used in advanced process control implementations to enhance productivity or energy efficiency. For this purpose, two approaches, namely k-means and self-organizing maps, were evaluated. Our results show that it is possible to identify operational regions delimited as clusters with consistent results.
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Briggs, Emma, Marc de Kamps, Willie Hamilton, Owen Johnson, Ciarán D. McInerney, and Richard D. Neal. "Machine Learning for Risk Prediction of Oesophago-Gastric Cancer in Primary Care: Comparison with Existing Risk-Assessment Tools." Cancers 14, no. 20 (October 14, 2022): 5023. http://dx.doi.org/10.3390/cancers14205023.
Анотація:
Oesophago-gastric cancer is difficult to diagnose in the early stages given its typical non-specific initial manifestation. We hypothesise that machine learning can improve upon the diagnostic performance of current primary care risk-assessment tools by using advanced analytical techniques to exploit the wealth of evidence available in the electronic health record. We used a primary care electronic health record dataset derived from the UK General Practice Research Database (7471 cases; 32,877 controls) and developed five probabilistic machine learning classifiers: Support Vector Machine, Random Forest, Logistic Regression, Naïve Bayes, and Extreme Gradient Boosted Decision Trees. Features included basic demographics, symptoms, and lab test results. The Logistic Regression, Support Vector Machine, and Extreme Gradient Boosted Decision Tree models achieved the highest performance in terms of accuracy and AUROC (0.89 accuracy, 0.87 AUROC), outperforming a current UK oesophago-gastric cancer risk-assessment tool (ogRAT). Machine learning also identified more cancer patients than the ogRAT: 11.0% more with little to no effect on false positives, or up to 25.0% more with a slight increase in false positives (for Logistic Regression, results threshold-dependent). Feature contribution estimates and individual prediction explanations indicated clinical relevance. We conclude that machine learning could improve primary care cancer risk-assessment tools, potentially helping clinicians to identify additional cancer cases earlier. This could, in turn, improve survival outcomes.
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Reddy, Cherukupally Karunakar, Suraj Janjirala, and Kevulothu Bhanu Prakash. "Gesture Controlled Virtual Mouse with the Support of Voice Assistant." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 2314–20. http://dx.doi.org/10.22214/ijraset.2022.44323.
Анотація:
Abstract: This work offers a cursor control system that utilises a web cam to capture human movements and a voice assistant to quickly traverse system controls. Using MediaPipe, the system will let the user to navigate the computer cursor with their hand motions. It will use various hand motions to conduct activities such as left click and dragging. It also allows you to choose numerous items, adjust the volume, and adjust the brightness. MediaPipe, OpenCV etc advanced libraries in python are used to build the system. A hand gesture and a voice assistant used to physically control all i/o operations. To recognise hand gestures and vocal instructions, the project employs cutting-edge machine learning and computer vision techniques, which operate effectively without the need for extra computer hardware. Hand gestures are a simple and natural way to communicate. Keywords: MediaPipe, Gesture Recognition, Voice Assistant, Machine Learning, Virtual Mouse
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Su, Kuo-Min, Tzu-Wei Lin, Li-Chun Liu, Yi-Pin Yang, Mong-Lien Wang, Ping-Hsing Tsai, Peng-Hui Wang, Mu-Hsien Yu, Chia-Ming Chang, and Cheng-Chang Chang. "The Potential Role of Complement System in the Progression of Ovarian Clear Cell Carcinoma Inferred from the Gene Ontology-Based Immunofunctionome Analysis." International Journal of Molecular Sciences 21, no. 8 (April 17, 2020): 2824. http://dx.doi.org/10.3390/ijms21082824.
Анотація:
Ovarian clear cell carcinoma (OCCC) is the second most common epithelial ovarian carcinoma (EOC). It is refractory to chemotherapy with a worse prognosis after the preliminary optimal debulking operation, such that the treatment of OCCC remains a challenge. OCCC is believed to evolve from endometriosis, a chronic immune/inflammation-related disease, so that immunotherapy may be a potential alternative treatment. Here, gene set-based analysis was used to investigate the immunofunctionomes of OCCC in early and advanced stages. Quantified biological functions defined by 5917 Gene Ontology (GO) terms downloaded from the Gene Expression Omnibus (GEO) database were used. DNA microarray gene expression profiles were used to convert 85 OCCCs and 136 normal controls into to the functionome. Relevant offspring were as extracted and the immunofunctionomes were rebuilt at different stages by machine learning. Several dysregulated pathogenic functions were found to coexist in the immunopathogenesis of early and advanced OCCC, wherein the complement-activation-alternative-pathway may be the headmost dysfunctional immunological pathway in duality for carcinogenesis at all OCCC stages. Several immunological genes involved in the complement system had dual influences on patients’ survival, and immunohistochemistrical analysis implied the higher expression of C3a receptor (C3aR) and C5a receptor (C5aR) levels in OCCC than in controls.
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Butt, Shahid Ikramullah, Umer Asgher, Umar Mushtaq, Riaz Ahmed, Faping Zhang, Yasar Ayaz, Mohsin Jamil, and Muhammad Kamal Amjad. "Intelligent Machine Vision Based Modeling and Positioning System in Sand Casting Process." Advances in Materials Science and Engineering 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/3192672.
Анотація:
Advanced vision solutions enable manufacturers in the technology sector to reconcile both competitive and regulatory concerns and address the need for immaculate fault detection and quality assurance. The modern manufacturing has completely shifted from the manual inspections to the machine assisted vision inspection methodology. Furthermore, the research outcomes in industrial automation have revolutionized the whole product development strategy. The purpose of this research paper is to introduce a new scheme of automation in the sand casting process by means of machine vision based technology for mold positioning. Automation has been achieved by developing a novel system in which casting molds of different sizes, having different pouring cup location and radius, position themselves in front of the induction furnace such that the center of pouring cup comes directly beneath the pouring point of furnace. The coordinates of the center of pouring cup are found by using computer vision algorithms. The output is then transferred to a microcontroller which controls the alignment mechanism on which the mold is placed at the optimum location.
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Karagiannopoulos, Stavros, Athanasios Vasilakis, Panos Kotsampopoulos, Nikos Hatziargyriou, Petros Aristidou, and Gabriela Hug. "Experimental Verification of Self-Adapting Data-Driven Controllers in Active Distribution Grids." Energies 14, no. 10 (May 14, 2021): 2837. http://dx.doi.org/10.3390/en14102837.
Анотація:
Lately, data-driven algorithms have been proposed to design local controls for Distributed Generators (DGs) that can emulate the optimal behaviour without any need for communication or centralised control. The design is based on historical data, advanced off-line optimization techniques and machine learning methods, and has shown great potential when the operating conditions are similar to the training data. However, safety issues arise when the real-time conditions start to drift away from the training set, leading to the need for online self-adapting algorithms and experimental verification of data-driven controllers. In this paper, we propose an online self-adapting algorithm that adjusts the DG controls to tackle local power quality issues. Furthermore, we provide experimental verification of the data-driven controllers through power Hardware-in-the-Loop experiments using an industrial inverter. The results presented for a low-voltage distribution network show that data-driven schemes can emulate the optimal behaviour and the online modification scheme can mitigate local power quality issues.
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Sravani, P., Shaik Chand Mabhu Subhani, and N. Vijay Kumar. "Developing Program Code for Automatic Color Code Sensing Punching Machine Using WPL Software." International Journal of Innovative Research in Engineering and Management 9, no. 6 (December 30, 2022): 119–25. http://dx.doi.org/10.55524/ijirem.2022.9.6.21.
Анотація:
This paper presents the idea of developing a logic or program code for an Automatic Color code sensing and punching machine which is driven based on Pneumatic architecture and can be used at the packing section in industries where the end user can punch labels on the objects which are moving on the conveyor based on their color. The program code basically controls the Pneumatic valves present in the system which actuates the Cylinders and helps in clamping and de clamping of moving objects and henceforth achieving the label at the required spot, whereas the desired color is acquired from the dedicated color sensor which helps in deciding the labeling process. This paper uses the advanced industrial controller (PLC) software called WPL Soft which is the most widely used tool in industries. This software requires a dedicated programming language called Ladder diagram, which is the 80% preferred programming language worldwide for programming PLCs. A program has been developed for automatically creating the application for color sensing and punching label on the desired objects based on color.
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Jones, W. D., and A. R. Fletcher. "Electric Drives on the LV100 Gas Turbine Engine." Journal of Engineering for Gas Turbines and Power 116, no. 2 (April 1, 1994): 411–17. http://dx.doi.org/10.1115/1.2906836.
Анотація:
The LV100 gas turbine engine is being developed for U.S. Army ground vehicle use. A unique approach for controls and accessories is being used whereby the engine has no accessory gearbox. Instead a high-speed starter/generator is mounted directly on the compressor shaft and powers all engine accessories as well as supplies the basic electrical power needs of the vehicle. This paper discusses the evolution of the electrically driven LV100 accessory system starting with the Advanced Integrated Propulsion System (AIPS) demonstrator program, through the current system to future possibilities with electric vehicle propulsion. Issues in electrical vehicle propulsion are discussed including machine type, electrical power type, and operation with a gas turbine.
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Hoecker, Douglas G., Kevin M. Corker, Emilie M. Roth, Melvin H. Lipner, and Marilyn S. Bunzo. "Man-Machine Design and Analysis System (MIDAS) Applied to a Computer-Based Procedure-Aiding System." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 38, no. 4 (October 1994): 195–99. http://dx.doi.org/10.1177/154193129403800402.
Анотація:
Difficult issues in design criteria confront the designers of human—computer interaction (HCI) implementations for future power plant control rooms. Such HCI-intensive control—room elements include “soft” controls and displays, computerized procedures, alarm presentations, and support for cooperative information—sharing among crewmembers. This shift in technology, from dedicated controls and displays in fixed locations to multifunction computer—driven operator workstations and wall displays, must focus not only on the required functionality of these interfaces, but also on their crafting and integration in such a way as to minimize the likelihood of operator error. With the objective of providing early insight into the cognitively error—prone consequences of selected interface dynamics, we are adapting a computer—based cognitive modeling tool, the Man—machine Integrated Design and Analysis System (MIDAS), to quantitatively model certain user requirements for operating different types of interfaces while dealing with high—consequence events in a control room setting. MIDAS was conceived and is being developed as a joint Army/NASA program at the NASA Ames Research Center to test different design approaches to computerizing the cockpits of advanced commercial and military aircraft. This report presents preliminary results from a project to adapt the MIDAS tool to the nuclear control room domain. These results have enabled comparative observation of cognitive loading depending on whether a supervisor uses computerized procedures or paper procedures to direct crew response to a plant trip event. The results suggest that each technology for procedural support, in its current respective implementation, has its own strengths and weaknesses at different points in the control task dialog.
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Medeiros, Jonathan Wagner de, Anthony José da Cunha Carneiro Lins, Oluwarotimi Williams Samuel, Elker Lene Santos de Lima, Maria Luiza Tabosa de Carvalho Galvão, Bárbara Oliveira Silva, Giwellington Silva Albuquerque, Luísa Priscilla Oliveira de Lima, and Maria Tereza Cartaxo Muniz. "Mbl-2 gene polymorphisms in pediatric Burkitt lymphoma: an approach based on machine learning techniques." Research, Society and Development 10, no. 12 (September 26, 2021): e444101220561. http://dx.doi.org/10.33448/rsd-v10i12.20561.
Анотація:
Introduction: Burkitt lymphoma belongs to the group of non-Hodgkin lymphomas. Although curable in 80% of less advanced stages, it presents in advanced stages in about 75% of cases in Brazil’s Northeast region, requiring urgent and intensive care in the early stages of treatment. Objectives: therefore, this study aimed to verify the participation of MBL-2 gene polymorphisms in the development of Burkitt lymphoma. Methods: In this article, computational approaches based on the Machine Learning technique were used, where we implemented the Random Forest and KMeans algorithms to classify patterns of individuals diagnosed with the disease and, therefore, differentiate them from healthy individuals. A group of 56 patients aged 0 to 18 years, with Burkitt lymphoma, from a reference hospital in the treatment of childhood cancer, was evaluated, together with a control group consisting of 150 samples, all of which were tested for exon 1 polymorphisms and the MBL2 gene -221 and -550 regions. Results: At first, an unsupervised classification was performed, which identified as two the number of groups that best represent the data present in our database, reaching 72.81% accuracy in the separation of patients and controls. Then, the supervised classification was performed, where the classifier obtained a 70.97% success rate, being possible to reach 75% accuracy in the best GridSearch configuration when performing a cross validation. Conclusion: It was not yet possible to conclude about the participation of the evaluated polymorphisms in the development of the BL, however the computational techniques used proved to be very promising for carrying out studies of this nature.
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Thabtah, Fadi, and David Peebles. "A new machine learning model based on induction of rules for autism detection." Health Informatics Journal 26, no. 1 (January 29, 2019): 264–86. http://dx.doi.org/10.1177/1460458218824711.
Анотація:
Autism spectrum disorder is a developmental disorder that describes certain challenges associated with communication (verbal and non-verbal), social skills, and repetitive behaviors. Typically, autism spectrum disorder is diagnosed in a clinical environment by licensed specialists using procedures which can be lengthy and cost-ineffective. Therefore, scholars in the medical, psychology, and applied behavioral science fields have in recent decades developed screening methods such as the Autism Spectrum Quotient and Modified Checklist for Autism in Toddlers for diagnosing autism and other pervasive developmental disorders. The accuracy and efficiency of these screening methods rely primarily on the experience and knowledge of the user, as well as the items designed in the screening method. One promising direction to improve the accuracy and efficiency of autism spectrum disorder detection is to build classification systems using intelligent technologies such as machine learning. Machine learning offers advanced techniques that construct automated classifiers that can be exploited by users and clinicians to significantly improve sensitivity, specificity, accuracy, and efficiency in diagnostic discovery. This article proposes a new machine learning method called Rules-Machine Learning that not only detects autistic traits of cases and controls but also offers users knowledge bases (rules) that can be utilized by domain experts in understanding the reasons behind the classification. Empirical results on three data sets related to children, adolescents, and adults show that Rules-Machine Learning offers classifiers with higher predictive accuracy, sensitivity, harmonic mean, and specificity than those of other machine learning approaches such as Boosting, Bagging, decision trees, and rule induction.
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Bhatt, Madan Lal Brahma, Vikas Kumar Srivastava, Durga Prasad Mishra, Madhu Mati Goel, Divya Mehrotra, Kirti Srivastava, and Rishi Kumar Gara. "Significance of plasma osteopontin (OPN) as a biomarker in squamous cell carcinoma of tongue." Journal of Clinical Oncology 31, no. 15_suppl (May 20, 2013): e17020-e17020. http://dx.doi.org/10.1200/jco.2013.31.15_suppl.e17020.
Анотація:
e17020 Background: Osteopontin (OPN) is an integrin-binding glycoprotein and its upregulation plays an important role in cancer progression. We analysed plasma OPN levels in squamous cell carcinoma of tongue (SCCT) patients, and healthy controls. We evaluated the relationship between plasma OPN level and outcome to therapy in the patients. Methods: Plasma OPN levels in 54 patients of SCCT and 18 healthy controls were measured by ELISA method. All participants gave written consent to participate in the study. Patients were subjected to chemo-radiotherapy using tele-cobalt radiotherapy machine (Theratron 780 C, AECL, Ottawa). A dose of 70 Gy of radiation was delivered to all the patients in 7 weeks with 2 Gy fraction size, 5 days a week by shrinking field technique. Chemotherapy was given in the form of Inj cisplatinum 30 mg/m2 IVI weekly, during the course of treatment with hydration, diuresis and anti-emetics. The data was analysed by using SPSS version 11.5 software. A value of p < 0.05 was considered statistically significant. Results: The plasma OPN levels of the SCCT ranged from 29.53 ng/mL- 514.52 ng/mL (mean and SE; 229.94 ± 21.96 ng/mL), which were significantly higher (p < 0.0001) than those of the controls which ranged from 12.45 ng/mL- 109.46 ng/mL (mean± SE; 50.69 ± 6.83 ng/mL). The patients with advanced T stage (T3/T4 vs.T1/T2) and positive N status (N +ve vs N-ve) had significantly higher plasma levels of OPN (p < 0.0001 and 0.001 respectively). The Kalpan-Meier survival curves for overall survival of patients with advanced T stage, positive N status or advanced TNM stage were significantly lower than that of patients with early T stage, negative N status or early TNM stage, respectively. Multivariate Cox regression analysis showed that OPN may be a predictive biomarker for poor outcome to radio-chemotherapy (95% CI = 0.081–0.293, p < 0.005). Conclusions: Our results suggested that the evaluation of plasma OPN levels may be a useful biomarker for diagnosis and prediction of response to radio-chemotherapy in SCCT.
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Saha, Ankita, Chanda Pathak, and Sourav Saha. "A Study of Machine Learning Techniques in Cryptography for Cybersecurity." American Journal of Electronics & Communication 1, no. 4 (June 7, 2021): 22–26. http://dx.doi.org/10.15864/ajec.1404.
Анотація:
The importance of cybersecurity is on the rise as we have become more technologically dependent on the internet than ever before. Cybersecurity implies the process of protecting and recovering computer systems, networks, devices, and programs from any cyber attack. Cyber attacks are an increasingly sophisticated and evolving danger to our sensitive data, as attackers employ new methods to circumvent traditional security controls. Cryptanalysis is mainly used to crack cryptographic security systems and gain access to the contents of the encrypted messages, even if the key is unknown. It focuses on deciphering the encrypted data as it works with ciphertext, ciphers, and cryptosystems to understand how they work and find techniques for weakening them. For classical cryptanalysis, the recovery of ciphertext is difficult as the time complexity is exponential. The traditional cryptanalysis requires a significant amount of time, known plaintexts, and memory. Machine learning may reduce the computational complexity in cryptanalysis. Machine learning techniques have recently been applied in cryptanalysis, steganography, and other data-securityrelated applications. Deep learning is an advanced field of machine learning which mainly uses deep neural network architecture. Nowadays, deep learning techniques are usually explored extensively to solve many challenging problems of artificial intelligence. But not much work has been done on deep learning-based cryptanalysis. This paper attempts to summarize various machine learning based approaches for cryptanalysis along with discussions on the scope of application of deep learning techniques in cryptography.
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P. Chandru and R. Vadivel. "IOT-based intelligent drainage and dust identification system for enhanced hygiene and efficiency." World Journal of Advanced Research and Reviews 21, no. 3 (March 30, 2024): 1789–97. http://dx.doi.org/10.30574/wjarr.2024.21.3.0683.
Анотація:
In this abstract, we propose an innovative IoT-based Intelligent Drainage and Dust Identification System aimed at improving hygiene and operational efficiency across diverse environments. By leveraging interconnected sensors, actuators, and intelligent algorithms, the system automates drainage issue detection and dust accumulation identification. Sensors monitor fluid levels, detect blockages, and identify leaks, triggering timely responses such as alert notifications or automated valve controls. Concurrently, advanced image processing and optical sensors monitor dust accumulation, enabling real-time cleanliness assessment and automated cleaning when needed. Machine learning algorithms enhance system intelligence, continuously learning from data to optimize resource utilization and adapt to changing conditions. Versatile and applicable across residential, commercial, and industrial settings, this system promises to elevate hygiene standards, minimize disruptions, and optimize efficiency with its proactive approach to maintenance and cleanliness.
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YAMAMOTO, Takashi, Michihiro OSHIMA, Yoshio YOKOYAMA, and Toshio MIYAGI. "High-precision Grinding System utilizing Machine Vision Control Unit(Advanced machine tool)." Proceedings of International Conference on Leading Edge Manufacturing in 21st century : LEM21 2005.2 (2005): 439–42. http://dx.doi.org/10.1299/jsmelem.2005.2.439.
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Dankis, Novita Dewi Vebriyana, and Mulyono Mulyono. "RISK ASSESSMENT PERUSAHAAN EXPORT SEPATU PADA BAGIAN LINE UPPER PT. X." Indonesian Journal of Occupational Safety and Health 4, no. 1 (January 1, 2015): 22. http://dx.doi.org/10.20473/ijosh.v4i1.2015.22-32.
Анотація:
ABSTRACTRevolution in the industry sector has been rapidly grown to fill up all the needs of the consumer products. One involves supporting advanced machinery such as “Cutting, Skiving, Stitching, Emboss Logo, Roving, Punch Hole, Juki, BrushingEdge, Hammer Over Lapping and Two Molding”. In the factory production process, there are various types of high-risk activities, especially on line upper. The main of this research is to study the risk assessment on export companies line the upper part of the shoes export company using Job Safety Analysis. This research was conducted observational crosssectional design. Observations made to the hazards and control measures. Interviews were conducted to 12 employees. Variables in this research is production activity, hazard identification, risk assessment, risk control and residual risk. The results of hazard identification has been done, there are 91 known potential hazards, for risk assessment found 7 high risk and low risk 5. Machine classified as high risk on the risk assessment is roving machine, whereas low-risk is two molding machine. Control efforts on the upper line in accordance with the hierarchy of controlling a number of 91 controls, whereas for the residual risk still remains as much as 30 residual risk. Control has been applied quite well by pressing the consequences of hazards and risk management.Keywords: risk assessment, controlling, residual risk
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Chang, Kuo-Hsuan, Mei-Ling Cheng, Hsiang-Yu Tang, Cheng-Yu Huang, Hsiu-Chuan Wu, and Chiung-Mei Chen. "Alterations of Sphingolipid and Phospholipid Pathways and Ornithine Level in the Plasma as Biomarkers of Parkinson’s Disease." Cells 11, no. 3 (January 24, 2022): 395. http://dx.doi.org/10.3390/cells11030395.
Анотація:
The biomarkers of Parkinson’s disease (PD) remain to be investigated. This work aimed to identify blood biomarkers for PD using targeted metabolomics analysis. We quantified the plasma levels of 255 metabolites in 92 PD patients and 60 healthy controls (HC). PD patients were sub-grouped into early (Hoehn–Yahr stage ≤ 2, n = 72) and advanced (Hoehn–Yahr stage > 2, n = 20) stages. Fifty-nine phospholipids, 3 fatty acids, 3 amino acids, and 7 biogenic amines, demonstrated significant alterations in PD patients. Six of them, dihydro sphingomyelin (SM) 24:0, 22:0, 20:0, phosphatidylethanolamine-plasmalogen (PEp) 38:6, and phosphatidylcholine 38:5 and 36:6, demonstrated lowest levels in PD patients in the advanced stage, followed by those in the early stage and HC. By contrast, the level of ornithine was highest in PD patients at the advanced stage, followed by those at the early stage and HC. These biomarker candidates demonstrated significant correlations with scores of motor disability, cognitive dysfunction, depression, and quality of daily life. The support vector machine algorithm using α-synuclein, dihydro SM 24:0, and PEp 38:6 demonstrated good ability to separate PD from HC (AUC: 0.820). This metabolomic analysis demonstrates new plasma biomarker candidates for PD and supports their role in participating PD pathogenesis and monitoring disease progression.
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Hajek, Tomas, Katja Franke, Marian Kolenic, Jana Capkova, Martin Matejka, Lukas Propper, Rudolf Uher, et al. "Brain Age in Early Stages of Bipolar Disorders or Schizophrenia." Schizophrenia Bulletin 45, no. 1 (December 20, 2017): 190–98. http://dx.doi.org/10.1093/schbul/sbx172.
Анотація:
Abstract Background The greater presence of neurodevelopmental antecedants may differentiate schizophrenia from bipolar disorders (BD). Machine learning/pattern recognition allows us to estimate the biological age of the brain from structural magnetic resonance imaging scans (MRI). The discrepancy between brain and chronological age could contribute to early detection and differentiation of BD and schizophrenia. Methods We estimated brain age in 2 studies focusing on early stages of schizophrenia or BD. In the first study, we recruited 43 participants with first episode of schizophrenia-spectrum disorders (FES) and 43 controls. In the second study, we included 96 offspring of bipolar parents (48 unaffected, 48 affected) and 60 controls. We used relevance vector regression trained on an independent sample of 504 controls to estimate the brain age of study participants from structural MRI. We calculated the brain-age gap estimate (BrainAGE) score by subtracting the chronological age from the brain age. Results Participants with FES had higher BrainAGE scores than controls (F(1, 83) = 8.79, corrected P = .008, Cohen’s d = 0.64). Their brain age was on average 2.64 ± 4.15 years greater than their chronological age (matched t(42) = 4.36, P < .001). In contrast, participants at risk or in the early stages of BD showed comparable BrainAGE scores to controls (F(2,149) = 1.04, corrected P = .70, η2 = 0.01) and comparable brain and chronological age. Conclusions Early stages of schizophrenia, but not early stages of BD, were associated with advanced BrainAGE scores. Participants with FES showed neurostructural alterations, which made their brains appear 2.64 years older than their chronological age. BrainAGE scores could aid in early differential diagnosis between BD and schizophrenia.
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Cai, Chenglin, Hongyu Li, Lijia Zhang, Junqi Li, Songqi Duan, Zhengfeng Fang, Cheng Li, et al. "Machine Learning Identification of Nutrient Intake Variations across Age Groups in Metabolic Syndrome and Healthy Populations." Nutrients 16, no. 11 (May 28, 2024): 1659. http://dx.doi.org/10.3390/nu16111659.
Анотація:
This study undertakes a comprehensive examination of the intricate link between diet nutrition, age, and metabolic syndrome (MetS), utilizing advanced artificial intelligence methodologies. Data from the National Health and Nutrition Examination Survey (NHANES) spanning from 1999 to 2018 were meticulously analyzed using machine learning (ML) techniques, specifically extreme gradient boosting (XGBoost) and the proportional hazards model (COX). Using these analytic methods, we elucidated a significant correlation between age and MetS incidence and revealed the impact of age-specific dietary patterns on MetS. The study delineated how the consumption of certain dietary components, namely retinol, beta-cryptoxanthin, vitamin C, theobromine, caffeine, lycopene, and alcohol, variably affects MetS across different age demographics. Furthermore, it was revealed that identical nutritional intakes pose diverse pathogenic risks for MetS across varying age brackets, with substances such as cholesterol, caffeine, and theobromine exhibiting differential risks contingent on age. Importantly, this investigation succeeded in developing a predictive model of high accuracy, distinguishing individuals with MetS from healthy controls, thereby highlighting the potential for precision in dietary interventions and MetS management strategies tailored to specific age groups. These findings underscore the importance of age-specific nutritional guidance and lay the foundation for future research in this area.
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Joutsijoki, Henry, Kirsi Penttinen, Martti Juhola, and Katriina Aalto-Setälä. "Separation of HCM and LQT Cardiac Diseases with Machine Learning of Ca2+ Transient Profiles." Methods of Information in Medicine 58, no. 04/05 (November 2019): 167–78. http://dx.doi.org/10.1055/s-0040-1701484.
Анотація:
Abstract Background Modeling human cardiac diseases with induced pluripotent stem cells not only enables to study disease pathophysiology and develop therapies but also, as we have previously showed, it can offer a tool for disease diagnostics. We previously observed that a few genetic cardiac diseases can be separated from each other and healthy controls by applying machine learning to Ca2+ transient signals measured from iPSC-derived cardiomyocytes (CMs). Objectives For the current research, 419 hypertrophic cardiomyopathy (HCM) transient signals and 228 long QT syndrome (LQTS) transient signals were measured. HCM signals included data recorded from iPSC-CMs carrying either α-tropomyosin, i.e., TPM1 (HCMT) or MYBPC3 or myosin-binding protein C (HCMM) mutation and LQTS signals included data recorded from iPSC-CMs carrying potassium voltage-gated channel subfamily Q member 1 (KCNQ1) mutation (long QT syndrome 1 [LQT1]) or KCNH2 mutation (long QT syndrome 2 [LQT2]). The main objective was to study whether and how effectively HCMM and HCMT can be separated from each other as well as LQT1 from LQT2. Methods After preprocessing those Ca2+ signals where we computed peak waveforms we then classified the two mutations of both disease pairs by using several different machine learning methods. Results We obtained excellent classification accuracies of 89% for HCM and even 100% for LQT at their best. Conclusion The results indicate that the methods applied would be efficient for the identification of these genetic cardiac diseases.
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Yang, Xiao Lan, Ji Feng Liu, Meng Nan Si, Jia Wei Li, and Biao Huang. "Advance Control Experiment of Vibration Machine with High Vibration Intensity Based on SCM – Dynamics Characteristics and Advanced Control." Advanced Materials Research 912-914 (April 2014): 554–58. http://dx.doi.org/10.4028/www.scientific.net/amr.912-914.554.
Анотація:
The vibration-stress field could be formed by high vibration intensity in vibration machine to improve the ability of the collision, shock, shear and extrusion for the system, and it also can avoid plugging screen for vibration screening machine, which could make for solving some special requirements of the vibration machine. To research the vibration machines strongly nonlinear and high vibration intensity characteristic such as certain excitation and uncertain response, the vibration machine with its double-mass is built, and its vibration exciter uses two partial blocks as vibration motor. In addition, dynamic vibration differential equation is established. To achieve high vibration intensity results based on the vibration machines safe working, the advanced control based on the SCM and Intelligent frequency conversion is put forward, and the advanced control system with its host computer, frequency converter, SCM, charge-amplifier, sensor and the vibration machine is been established.
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Xin, Guangda, Guangyu Zhou, Wenlong Zhang, and Xiaofei Zhang. "Construction and Validation of Predictive Model to Identify Critical Genes Associated with Advanced Kidney Disease." International Journal of Genomics 2020 (November 12, 2020): 1–12. http://dx.doi.org/10.1155/2020/7524057.
Анотація:
Background. Chronic kidney disease (CKD) is characterized by progressive renal function loss, which may finally lead to end-stage renal disease (ESRD). The study is aimed at identifying crucial genes related to CKD progressive and constructing a disease prediction model to investigate risk factors. Methods. GSE97709 and GSE37171 datasets were downloaded from the GEO database including peripheral blood samples from subjects with CKD, ESRD, and healthy controls. Differential expressed genes (DEGs) were identified and functional enrichment analysis. Machine learning algorithm-based prediction model was constructed to identify crucial functional feature genes related to ESRD. Results. A total of 76 DEGs were screened from CDK vs. normal samples while 10,114 DEGs were identified from ESRD vs. CDK samples. For numerous genes related to ESRD, several GO biological terms and 141 signaling pathways were identified including markedly upregulated olfactory transduction and downregulated platelet activation pathway. The DEGs were clustering in three modules according to WGCNA access, namely, ME1, ME2, and ME3. By construction of the XGBoost model and dataset validation, we screened cohorts of genes associated with progressive CKD, such as FZD10, FOXD4, and FAM215A. FZD10 represented the highest score ( F score = 21) in predictive model. Conclusion. Our results demonstrated that FZD10, FOXD4, PPP3R1, and UCP2 might be critical genes in CKD progression.
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Sardar, Suman Kalyan, Biswajit Sarkar, and Byunghoon Kim. "Integrating Machine Learning, Radio Frequency Identification, and Consignment Policy for Reducing Unreliability in Smart Supply Chain Management." Processes 9, no. 2 (January 29, 2021): 247. http://dx.doi.org/10.3390/pr9020247.
Анотація:
Adopting smart technologies for supply chain management leads to higher profits. The manufacturer and retailer are two supply chain players, where the retailer is unreliable and may not send accurate demand information to the manufacturer. As an advanced smart technology, Radio Frequency Identification (RFID) is implemented to track and trace each product’s movement on a real-time basis in the inventory. It takes this supply chain to a smart supply chain management. This research proposes a Machine Learning (ML) approach for on-demand forecasting under smart supply chain management. Using Long-Short-Term Memory (LSTM), the demand is forecasted to obtain the exact demand information to reduce the overstock or understock situation. A measurement for the environmental effect is also incorporated with the model. A consignment policy is applied where the manufacturer controls the inventory, and the retailer gets a fixed fee along with a commission for selling each product. The manufacturer installs RFID technology at the retailer’s place. Two mathematical models are solved using a classical optimization technique. The results from those two models show that the ML-RFID model gives a higher profit than the existing traditional system.
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Ma, Da, Evangeline Yee, Jane K. Stocks, Lisanne M. Jenkins, Karteek Popuri, Guillaume Chausse, Lei Wang, Stephan Probst, and Mirza Faisal Beg. "Blinded Clinical Evaluation for Dementia of Alzheimer’s Type Classification Using FDG-PET: A Comparison Between Feature-Engineered and Non-Feature-Engineered Machine Learning Methods." Journal of Alzheimer's Disease 80, no. 2 (March 23, 2021): 715–26. http://dx.doi.org/10.3233/jad-201591.
Анотація:
Background: Advanced machine learning methods can aid in the identification of dementia risk using neuroimaging-derived features including FDG-PET. However, to enable the translation of these methods and test their usefulness in clinical practice, it is crucial to conduct independent validation on real clinical samples, which has yet to be properly delineated in the current literature. Objective: In this paper, we present our efforts to enable such clinical translational through the evaluation and comparison of two machine-learning methods for discrimination between dementia of Alzheimer’s type (DAT) and Non-DAT controls. Methods: FDG-PET-based dementia scores were generated on an independent clinical sample whose clinical diagnosis was blinded to the algorithm designers. A feature-engineered approach (multi-kernel probability classifier) and a non-feature-engineered approach (3D convolutional neural network) were analyzed. Both classifiers were pre-trained on cognitively normal subjects as well as subjects with DAT. These two methods provided a probabilistic dementia score for this previously unseen clinical data. Performance of the algorithms were compared against ground-truth dementia rating assessed by experienced nuclear physicians. Results: Blinded clinical evaluation on both classifiers showed good separation between the cognitively normal subjects and the patients diagnosed with DAT. The non-feature-engineered dementia score showed higher sensitivity among subjects whose diagnosis was in agreement between the machine-learning models, while the feature-engineered approach showed higher specificity in non-consensus cases. Conclusion: In this study, we demonstrated blinded evaluation using data from an independent clinical sample for assessing the performance in DAT classification models in a clinical setting. Our results showed good generalizability for two machine-learning approaches, marking an important step for the translation of pre-trained machine-learning models into clinical practice.
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Cerrone, Rosaria. "Are Artificial Intelligence and Machine Learning Shaping a New Risk Management Approach?" International Business Research 16, no. 12 (November 30, 2023): 82. http://dx.doi.org/10.5539/ibr.v16n12p82.
Анотація:
Digital revolution is influencing many economic sectors and for a few years banking sector is under a great transformation mainly due to the development and the use of new technologies. The most recent ones are artificial intelligence (AI) with the recourse to advanced algorithms. The main banking services, their offer, but above all, the customer relations have been significantly influenced by the this. The recourse to new channels, the monitoring of risks and the controls of frauds are only some of the applications of machine learning (ML). To manage the increase in financial and non-financial risks AI and ML seem to give a great help to banks. The survey conducted from December 2022 to May 2023 with a sample of Italian banks of different size, shows the level of awareness in the recourse to these technologies. Moreover, it aims to assess the maturity and the future perspectives in the adoption of AI in the financial system. The analysis is divided into different investigation areas that show how banks can mitigate the risks involved with the implementation of AI and how it affects the risk management process. The paper covers the gap in literature where AI and ML are mainly considered as separate tools to face specific banking projects; and Italian banks, even if with differences due to the size, are aware of the relevance of these new technologies. The research is a contribute to the discussion about the application of AI and ML in a holistic dimension.
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Jin, Bo, Yue Qu, Liang Zhang, and Zhan Gao. "Diagnosing Parkinson Disease Through Facial Expression Recognition: Video Analysis." Journal of Medical Internet Research 22, no. 7 (July 10, 2020): e18697. http://dx.doi.org/10.2196/18697.
Анотація:
Background The number of patients with neurological diseases is currently increasing annually, which presents tremendous challenges for both patients and doctors. With the advent of advanced information technology, digital medical care is gradually changing the medical ecology. Numerous people are exploring new ways to receive a consultation, track their diseases, and receive rehabilitation training in more convenient and efficient ways. In this paper, we explore the use of facial expression recognition via artificial intelligence to diagnose a typical neurological system disease, Parkinson disease (PD). Objective This study proposes methods to diagnose PD through facial expression recognition. Methods We collected videos of facial expressions of people with PD and matched controls. We used relative coordinates and positional jitter to extract facial expression features (facial expression amplitude and shaking of small facial muscle groups) from the key points returned by Face++. Algorithms from traditional machine learning and advanced deep learning were utilized to diagnose PD. Results The experimental results showed our models can achieve outstanding facial expression recognition ability for PD diagnosis. Applying a long short-term model neural network to the positions of the key features, precision and F1 values of 86% and 75%, respectively, can be reached. Further, utilizing a support vector machine algorithm for the facial expression amplitude features and shaking of the small facial muscle groups, an F1 value of 99% can be achieved. Conclusions This study contributes to the digital diagnosis of PD based on facial expression recognition. The disease diagnosis model was validated through our experiment. The results can help doctors understand the real-time dynamics of the disease and even conduct remote diagnosis.
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Appelbaum, Limor, Jose Pablo Cambronero, Karla Pollick, George Silva, Jennifer P. Stevens, Harvey J. Mamon, Irving D. Kaplan, and Martin Rinard. "Development and validation of a pancreatic cancer prediction model from electronic health records using machine learning." Journal of Clinical Oncology 38, no. 4_suppl (February 1, 2020): 679. http://dx.doi.org/10.1200/jco.2020.38.4_suppl.679.
Анотація:
679 Background: Pancreatic Adenocarcinoma (PDAC) is often diagnosed at an advanced stage. We sought to develop a model for early PDAC prediction in the general population, using electronic health records (EHRs) and machine learning. Methods: We used three EHR datasets from Beth-Israel Deaconess Medical Center (BIDMC) and Partners Healthcare (PHC): 1. “BIDMC-Development-Data” (BIDMC-DD) for model development, using a feed-forward neural network (NN) and L2-regularized logistic regression,randomly split (80:20) into training and test groups. We tuned hyperparameters using cross-validation in training, and report performance on the test split. 2. “BIDMC-Large-Data” (BIDMC-LD) to re-fit and calibrate models. 3. “PHC-Data” for external validation. We evaluate using Area Under the Receiver Operating Characteristic Curve (AUC) and compute 95% CI using empirical bootstrap over test data. PDAC patients were selected using ICD9/-10 codes and validated with tumor registries. In contrast to prior work, we did not predefine feature sets based on known clinical correlates and instead employed data-driven feature selection, specifically importance-based feature pruning, regularization, and manual validation, to identify diagnostic-based features. Results: BIDMC-DD included demographics, diagnoses, labs and medications for 1018 patients (cases = 509; age-sex paired controls). BIDMC-LD included diagnoses for 547,917 patients (cases = 509), and PHC included diagnoses for 160,593 patients (cases = 408). We compared our approach to adapted and re-fitted published baselines. With a 365-day lead-time, NN obtained a BIDMC-DD test AUC of 0.84 (CI 0.79 - 0.90) versus the previous best baseline AUC of 0.70 (CI 0.62 - 0.78). We also validated using BIDMC-DD’s test cancer patients and BIDMC LD controls. The AUC was 0.71 (CI 0.67 - 0.76) at the 365-day cutoff. NN’s external validation AUC on PHC-Data was 0.71 (CI 0.63 - 0.79), outperforming an existing model’s AUC of 0.61 (CI 0.52 - 0.70) (Baecker et al, 2019). Conclusions: Models based on data-driven feature selection outperform models that use predefined sets of known clinical correlates and can help in early prediction of PDAC development.
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Dev, Kapil, Chris Jun Hui Ho, Renzhe Bi, Yik Weng Yew, Dinish U. S, Amalina Binte Ebrahim Attia, Mohesh Moothanchery, Steven Thng Tien Guan, and Malini Olivo. "Machine Learning Assisted Handheld Confocal Raman Micro-Spectroscopy for Identification of Clinically Relevant Atopic Eczema Biomarkers." Sensors 22, no. 13 (June 21, 2022): 4674. http://dx.doi.org/10.3390/s22134674.
Анотація:
Atopic dermatitis (AD) is a common chronic inflammatory skin dermatosis condition due to skin barrier dysfunction that causes itchy, red, swollen, and cracked skin. Currently, AD severity clinical scores are subjected to intra- and inter-observer differences. There is a need for an objective scoring method that is sensitive to skin barrier differences. The aim of this study was to evaluate the relevant skin chemical biomarkers in AD patients. We used confocal Raman micro-spectroscopy and advanced machine learning methods as means to classify eczema patients and healthy controls with sufficient sensitivity and specificity. Raman spectra at different skin depths were acquired from subjects’ lower volar forearm location using an in-house developed handheld confocal Raman micro-spectroscopy system. The Raman spectra corresponding to the skin surface from all the subjects were further analyzed through partial least squares discriminant analysis, a binary classification model allowing the classification between eczema and healthy subjects with a sensitivity and specificity of 0.94 and 0.85, respectively, using stratified K-fold (K = 10) cross-validation. The variable importance in the projection score from the partial least squares discriminant analysis classification model further elucidated the role of important stratum corneum proteins and lipids in distinguishing two subject groups.
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T., Ahila, and A. C. Subha Jini. "A Comparative Study of HARR Feature Extraction and Machine Learning Algorithms for Covid-19 X-Ray Image Classification." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 6s (June 13, 2023): 475–82. http://dx.doi.org/10.17762/ijritcc.v11i6s.6955.
Анотація:
In this study, we investigated how effectively COVID-19 image categorization using Harr feature extraction and machine learning algorithms. We were particularly interested in the effectiveness of these algorithms. A dataset of 500 X-ray scans, equally split between 250 COVID-19-positive cases and 250 healthy controls, served as the basis for our study. K-nearest neighbors,decision tree, Linear regression, support vector machine, regression, classification, naive Bayes,random forest, as well as linear discriminant analysis were among the seven machine-learning approaches used to categorize the photos. With the use of Harr feature extraction, the features of the pictures were extracted. We studied the efficacy of COVID-19 X-ray images for classification utilizing the combination of machine learning as well as the Harr feature extraction methods in the present investigation due to their effectiveness. We searched a database of 500 X-rays for this investigation, dividing them equally between groups of 250 patients with COVID-19-positive cases and 250 healthy people. Following that, the images were examined using seven various machine learning approaches for recognition. These methods included naive Bayes, linear discriminant analysis, random forests, classification,k-nearest neighbors, and regression trees. The information from the photos was gathered using the Harr feature extraction method. The effectiveness of the algorithms was evaluated with the help of a variety of metrics, such asF1 score, precision,accuracy, recall, the area under the ROC curve, and the region of interest curve. According to our research, the Support Vector Machine algorithm had the highest accuracy, at 77%, while the Naive Bayes approach had the lowest accuracy, at 58%. By using machine learning and Harr feature extraction approaches, the Random Forest method yields the best results, based on our research. The development of future COVID-19 X-ray image-based automated diagnostic systems may be influenced by these findings. Results from the suggested model were comparable to those of cutting-edge models trained using transfer learning techniques. The proposed model's main advantage is that it has ten times fewer parameters than the most advanced models.A receiver operating characteristic (ROC) curve's F1 score, and the algorithms' accuracy, precision, the area under the curve, and recall were all used as metrics. According to our findings, the Naive Bayes method gained the least accuracy (58%) and the Support Vector Machine method produced the highest accuracy (77%) when used. Our results reveal that employing Harr feature extraction and machine learning techniques, the Random Forest strategy is the most successful way to recognize COVID-19 X-ray pictures. These findings may be pertinent to the development of automated COVID-19 diagnosis tools relying on X-ray images. The recommended model produced results that were competitive when measured against cutting-edge models trained using transfer learning techniques. The suggested model employs 10 times fewer parameters than the most advanced models, which is its key selling point.