Displaying publications 1 - 20 of 353 in total

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  1. Interactive knee cartilage extraction using efficient segmentation software: data from the osteoarthritis initiative
    Gan HS, Tan TS, Wong LX, Tham WK, Sayuti KA, Abdul Karim AH, et al.
    Biomed Mater Eng, 2014;24(6):3145-57.
    PMID: 25227024 DOI: 10.3233/BME-141137
    In medical image segmentation, manual segmentation is considered both labor- and time-intensive while automated segmentation often fails to segment anatomically intricate structure accordingly. Interactive segmentation can tackle shortcomings reported by previous segmentation approaches through user intervention. To better reflect user intention, development of suitable editing functions is critical. In this paper, we propose an interactive knee cartilage extraction software that covers three important features: intuitiveness, speed, and convenience. The segmentation is performed using multi-label random walks algorithm. Our segmentation software is simple to use, intuitive to normal and osteoarthritic image segmentation and efficient using only two third of manual segmentation's time. Future works will extend this software to three dimensional segmentation and quantitative analysis.
    Matched MeSH terms: Artificial Intelligence
  2. Water treatment and artificial intelligence techniques: a systematic literature review research
    Ismail W, Niknejad N, Bahari M, Hendradi R, Zaizi NJM, Zulkifli MZ
    Environ Sci Pollut Res Int, 2023 Jun;30(28):71794-71812.
    PMID: 34609681 DOI: 10.1007/s11356-021-16471-0
    As clean water can be considered among the essentials of human life, there is always a requirement to seek its foremost and high quality. Water primarily becomes polluted due to organic as well as inorganic pollutants, including nutrients, heavy metals, and constant contamination with organic materials. Predicting the quality of water accurately is essential for its better management along with controlling pollution. With stricter laws regarding water treatment to remove organic and biologic materials along with different pollutants, looking for novel technologic procedures will be necessary for improved control of the treatment processes by water utilities. Linear regression-based models with relative simplicity considering water prediction have been typically used as available statistical models. Nevertheless, in a majority of real problems, particularly those associated with modeling of water quality, non-linear patterns will be observed, requiring non-linear models to address them. Thus, artificial intelligence (AI) can be a good candidate in modeling and optimizing the elimination of pollutants from water in empirical settings with the ability to generate ideal operational variables, due to its recent considerable advancements. Management and operation of water treatment procedures are supported technically by these technologies, leading to higher efficiency compared to sole dependence on human operations. Thus, establishing predictive models for water quality and subsequently, more efficient management of water resources would be critically important, serving as a strong tool. A systematic review methodology has been employed in the present work to investigate the previous studies over the time interval of 2010-2020, while analyzing and synthesizing the literature, particularly regarding AI application in water treatment. A total number of 92 articles had addressed the topic under study using AI. Based on the conclusions, the application of AI can obviously facilitate operations, process automation, and management of water resources in significantly volatile contexts.
    Matched MeSH terms: Artificial Intelligence
  3. Fulltext Sudden event recognition: a survey
    Suriani NS, Hussain A, Zulkifley MA
    Sensors (Basel), 2013 Aug 05;13(8):9966-98.
    PMID: 23921828 DOI: 10.3390/s130809966
    Event recognition is one of the most active research areas in video surveillance fields. Advancement in event recognition systems mainly aims to provide convenience, safety and an efficient lifestyle for humanity. A precise, accurate and robust approach is necessary to enable event recognition systems to respond to sudden changes in various uncontrolled environments, such as the case of an emergency, physical threat and a fire or bomb alert. The performance of sudden event recognition systems depends heavily on the accuracy of low level processing, like detection, recognition, tracking and machine learning algorithms. This survey aims to detect and characterize a sudden event, which is a subset of an abnormal event in several video surveillance applications. This paper discusses the following in detail: (1) the importance of a sudden event over a general anomalous event; (2) frameworks used in sudden event recognition; (3) the requirements and comparative studies of a sudden event recognition system and (4) various decision-making approaches for sudden event recognition. The advantages and drawbacks of using 3D images from multiple cameras for real-time application are also discussed. The paper concludes with suggestions for future research directions in sudden event recognition.
    Matched MeSH terms: Artificial Intelligence*
  4. Incorporating intelligence into exit choice model for typical evacuation
    Mohammed Shuaib, Zarita Zainuddin
    Sains Malaysiana, 2017;46:1997-2005.
    Integrating an exit choice model into a microscopic crowd dynamics model is an essential approach for obtaining more
    efficient evacuation model. We describe various aspects of decision-making capability of an existing rule-based exit
    choice model for evacuation processes. In simulations, however, the simulated evacuees clogging at exits have behaved
    non-intelligently, namely they do not give up their exits for better ones for safer egress. We refine the model to endow
    the individuals with the ability to leave their exits due to dynamic changes by modifying the model of their excitement
    resulted from the source of panic. This facilitates the approximately equal crowd size at exits for being until the end
    of the evacuation process, and thereby the model accomplishes more optimal evacuation. For further intelligence, we
    introduce the prediction factor that enables higher probability of equally distributing evacuees at exits. A simulation to
    validate the contribution is performed, and the results are analyzed and compared with the original model.
    Matched MeSH terms: Intelligence
  5. Fulltext Oral cancer prognosis based on clinicopathologic and genomic markers using a hybrid of feature selection and machine learning methods
    Chang SW, Abdul-Kareem S, Merican AF, Zain RB
    BMC Bioinformatics, 2013;14:170.
    PMID: 23725313 DOI: 10.1186/1471-2105-14-170
    Machine learning techniques are becoming useful as an alternative approach to conventional medical diagnosis or prognosis as they are good for handling noisy and incomplete data, and significant results can be attained despite a small sample size. Traditionally, clinicians make prognostic decisions based on clinicopathologic markers. However, it is not easy for the most skilful clinician to come out with an accurate prognosis by using these markers alone. Thus, there is a need to use genomic markers to improve the accuracy of prognosis. The main aim of this research is to apply a hybrid of feature selection and machine learning methods in oral cancer prognosis based on the parameters of the correlation of clinicopathologic and genomic markers.
    Matched MeSH terms: Artificial Intelligence*
  6. A Systematic Review for Human EEG Brain Signals Based Emotion Classification, Feature Extraction, Brain Condition, Group Comparison
    Hamada M, Zaidan BB, Zaidan AA
    J Med Syst, 2018 Jul 24;42(9):162.
    PMID: 30043178 DOI: 10.1007/s10916-018-1020-8
    The study of electroencephalography (EEG) signals is not a new topic. However, the analysis of human emotions upon exposure to music considered as important direction. Although distributed in various academic databases, research on this concept is limited. To extend research in this area, the researchers explored and analysed the academic articles published within the mentioned scope. Thus, in this paper a systematic review is carried out to map and draw the research scenery for EEG human emotion into a taxonomy. Systematically searched all articles about the, EEG human emotion based music in three main databases: ScienceDirect, Web of Science and IEEE Xplore from 1999 to 2016. These databases feature academic studies that used EEG to measure brain signals, with a focus on the effects of music on human emotions. The screening and filtering of articles were performed in three iterations. In the first iteration, duplicate articles were excluded. In the second iteration, the articles were filtered according to their titles and abstracts, and articles outside of the scope of our domain were excluded. In the third iteration, the articles were filtered by reading the full text and excluding articles outside of the scope of our domain and which do not meet our criteria. Based on inclusion and exclusion criteria, 100 articles were selected and separated into five classes. The first class includes 39 articles (39%) consists of emotion, wherein various emotions are classified using artificial intelligence (AI). The second class includes 21 articles (21%) is composed of studies that use EEG techniques. This class is named 'brain condition'. The third class includes eight articles (8%) is related to feature extraction, which is a step before emotion classification. That this process makes use of classifiers should be noted. However, these articles are not listed under the first class because these eight articles focus on feature extraction rather than classifier accuracy. The fourth class includes 26 articles (26%) comprises studies that compare between or among two or more groups to identify and discover human emotion-based EEG. The final class includes six articles (6%) represents articles that study music as a stimulus and its impact on brain signals. Then, discussed the five main categories which are action types, age of the participants, and number size of the participants, duration of recording and listening to music and lastly countries or authors' nationality that published these previous studies. it afterward recognizes the main characteristics of this promising area of science in: motivation of using EEG process for measuring human brain signals, open challenges obstructing employment and recommendations to improve the utilization of EEG process.
    Matched MeSH terms: Artificial Intelligence*
  7. Learning disabilities in children
    Zabidi Azhar Mohd. Hussin
    Malaysian Journal of Child Health, 1998;10(1):9-18.
    MyJurnal
    Learning disability occurs in 10-15% of children. It is manifested by an imperfect ability to listen, think, speak, read, write, spell, calculate or interact. It may be specific as in dyslexia, dyscalculia, dysgraphia or nonspecific learning disability. In the latter group, there may be under-achievement despite average or above-average-intelligence, slow learners and mental retardation. Factors that may cause learning disability include genetic abnormalities, antenatal and perinatal insults, abnormal growth and malnutrition in early childhood, parental mode of upbringing, poor opportunity for learning, physical illness and emotional and social problems. Meticulous history taking and physical examination is important to arrive at a proper diagnosis so that the most appropriate management is given, often involving professionals working as a team.
    Matched MeSH terms: Intelligence
  8. APPLICATION OF FUZZY DELPHI METHOD TO VALIDATE CONTENT OF SPIRITUAL INTELLIGENCE INSTRUMENT FOR MUSLIM NURSES
    Atarhim MA, Manap J, Mastor KA, Mokhtar MK, Yusof A, Zabidi AFM
    J Nurs Meas, 2023 Jun 01;31(2):202-218.
    PMID: 37277156 DOI: 10.1891/JNM-2021-0012
    Background & Purpose: This study aims to consolidate expert views and validated 371 items for developing spiritual intelligence instrument for Muslim nurses guided by the Spiritual Intelligence Model for Human Excellence (SIMHE). Methods: A Fuzzy Delphi Method (FDM) was used to validate these items and analyzed with Triangular Fuzzy Numbers and Defuzzification process. Views from 20 experts from three different backgrounds, namely, theology/Sufism, psychology and Islamic counseling, and evaluation and measurement, were also included in the validation process. Results: All items fulfilled the prerequisite of a threshold level of (d) ≤ 0.2, which obtained more than 75% of expert consensus and α-cut value ≥ of 0.5. Conclusion: The FDM analysis results indicated that all items could further validate the instrument using Rasch measurement analysis.
    Matched MeSH terms: Intelligence
  9. Comparison between Personality, Emotional, and Educational Outcomes of Multiple Mini Interviews and Personal Interview
    Yusoff MSB
    Oman Med J, 2020 Nov;35(6):e199.
    PMID: 33214913 DOI: 10.5001/omj.2020.90
    Objectives: This study investigated the outcomes of multiple mini interviews and personal interview on personality traits, emotional intelligence, perceived educational environment, and stressors.

    Methods: This is a comparative cross-sectional study on two cohorts of pre-clinical medical students who were selected by multiple mini interviews and personal interview, respectively. Their personality traits, emotional intelligence, perceived educational environment, and perceived stressors were measured using different measurement tools.

    Results: Multiple mini interviews and personal interview demonstrated a similar ability to recruit medical students with a high level of emotional intelligence. The main advantage of personal interviews over multiple mini interviews in terms of personality traits is that it recruited candidates who had a higher level of conscientiousness trait. The main advantage of multiple mini interviews over personal interview on the educational environment is that medical students chosen by multiple mini interviews had a higher level of satisfaction with social aspects of medical training. Regardless of admission processes, the medical students were equally vulnerable to psychological distress due to various stressful events throughout medical training particularly related to academic loads.

    Conclusions: This study provided evidence to support the outcomes that multiple mini interviews and personal interview have on medical students' emotional intelligence, personality traits, perceived educational environment, and perceived stressors during the pre-clinical medical training. Interestingly, personal interview had a better outcome on conscientiousness while multiple mini interviews had a better outcome on the social aspect.

    Matched MeSH terms: Emotional Intelligence
  10. Fulltext The outcomes that an interview-based medical school admission process has on academic performance, psychological health, personality traits, and emotional intelligence
    Yusoff MSB
    J Taibah Univ Med Sci, 2018 Dec;13(6):503-511.
    PMID: 31435370 DOI: 10.1016/j.jtumed.2018.09.003
    Objectives: This study investigated the outcomes that an interview-based medical school admission process has on academic performance, psychological health, personality traits, and emotional intelligence.

    Methods: A comparative cross-sectional study was conducted on the interviewed and non-interviewed cohorts. Their examination marks were obtained from the academic office, psychological health was measured by DASS-21, personality traits were measured by USMaP-15, and emotional intelligence was measured by USMEQ-17.

    Results: The interviewed cohort performed significantly better in the clinical examination than the non-interviewed cohort. Conversely, the non-interviewed cohort performed significantly better in the theoretical examination. Depression, anxiety, and stress level between the two cohorts showed no difference. The interviewed cohort demonstrated more desirable personality traits, higher emotional intelligence, and social competence than the non-interviewed cohort.

    Discussion: This study provides evidence to support the claim that the interview-based admission process has favourable outcomes on clinical performance, emotional intelligence, and personality traits. Several insights gained as a result of this study are discussed.

    Matched MeSH terms: Emotional Intelligence
  11. Theoretically-derived molecular descriptors important in human intestinal absorption
    Agatonovic-Kustrin S, Beresford R, Yusof AP
    J Pharm Biomed Anal, 2001 May;25(2):227-37.
    PMID: 11275432
    A quantitative structure-human intestinal absorption relationship was developed using artificial neural network (ANN) modeling. A set of 86 drug compounds and their experimentally-derived intestinal absorption values used in this study was gathered from the literature and a total of 57 global molecular descriptors, including constitutional, topological, chemical, geometrical and quantum chemical descriptors, calculated for each compound. A supervised network with radial basis transfer function was used to correlate calculated molecular descriptors with experimentally-derived measures of human intestinal absorption. A genetic algorithm was then used to select important molecular descriptors. Intestinal absorption values (IA%) were used as the ANN's output and calculated molecular descriptors as the inputs. The best genetic neural network (GNN) model with 15 input descriptors was chosen, and the significance of the selected descriptors for intestinal absorption examined. Results obtained with the model that was developed indicate that lipophilicity, conformational stability and inter-molecular interactions (polarity, and hydrogen bonding) have the largest impact on intestinal absorption.
    Matched MeSH terms: Artificial Intelligence
  12. Fulltext Human facial neural activities and gesture recognition for machine-interfacing applications
    Hamedi M, Salleh ShH, Tan TS, Ismail K, Ali J, Dee-Uam C, et al.
    Int J Nanomedicine, 2011;6:3461-72.
    PMID: 22267930 DOI: 10.2147/IJN.S26619
    The authors present a new method of recognizing different human facial gestures through their neural activities and muscle movements, which can be used in machine-interfacing applications. Human-machine interface (HMI) technology utilizes human neural activities as input controllers for the machine. Recently, much work has been done on the specific application of facial electromyography (EMG)-based HMI, which have used limited and fixed numbers of facial gestures. In this work, a multipurpose interface is suggested that can support 2-11 control commands that can be applied to various HMI systems. The significance of this work is finding the most accurate facial gestures for any application with a maximum of eleven control commands. Eleven facial gesture EMGs are recorded from ten volunteers. Detected EMGs are passed through a band-pass filter and root mean square features are extracted. Various combinations of gestures with a different number of gestures in each group are made from the existing facial gestures. Finally, all combinations are trained and classified by a Fuzzy c-means classifier. In conclusion, combinations with the highest recognition accuracy in each group are chosen. An average accuracy >90% of chosen combinations proved their ability to be used as command controllers.
    Matched MeSH terms: Artificial Intelligence*
  13. Be Careful Where You Smile: Culture Shapes Judgments of Intelligence and Honesty of Smiling Individuals
    Krys K, -Melanie Vauclair C, Capaldi CA, Lun VM, Bond MH, Domínguez-Espinosa A, et al.
    Journal of nonverbal behavior, 2015 12 30;40:101-116.
    PMID: 27194817
    Smiling individuals are usually perceived more favorably than non-smiling ones-they are judged as happier, more attractive, competent, and friendly. These seemingly clear and obvious consequences of smiling are assumed to be culturally universal, however most of the psychological research is carried out in WEIRD societies (Western, Educated, Industrialized, Rich, and Democratic) and the influence of culture on social perception of nonverbal behavior is still understudied. Here we show that a smiling individual may be judged as less intelligent than the same non-smiling individual in cultures low on the GLOBE's uncertainty avoidance dimension. Furthermore, we show that corruption at the societal level may undermine the prosocial perception of smiling-in societies with high corruption indicators, trust toward smiling individuals is reduced. This research fosters understanding of the cultural framework surrounding nonverbal communication processes and reveals that in some cultures smiling may lead to negative attributions.
    Matched MeSH terms: Intelligence
  14. Fulltext Sign language recognition using the fusion of image and hand landmarks through multi-headed convolutional neural network
    Pathan RK, Biswas M, Yasmin S, Khandaker MU, Salman M, Youssef AAF
    Sci Rep, 2023 Oct 09;13(1):16975.
    PMID: 37813932 DOI: 10.1038/s41598-023-43852-x
    Sign Language Recognition is a breakthrough for communication among deaf-mute society and has been a critical research topic for years. Although some of the previous studies have successfully recognized sign language, it requires many costly instruments including sensors, devices, and high-end processing power. However, such drawbacks can be easily overcome by employing artificial intelligence-based techniques. Since, in this modern era of advanced mobile technology, using a camera to take video or images is much easier, this study demonstrates a cost-effective technique to detect American Sign Language (ASL) using an image dataset. Here, "Finger Spelling, A" dataset has been used, with 24 letters (except j and z as they contain motion). The main reason for using this dataset is that these images have a complex background with different environments and scene colors. Two layers of image processing have been used: in the first layer, images are processed as a whole for training, and in the second layer, the hand landmarks are extracted. A multi-headed convolutional neural network (CNN) model has been proposed and tested with 30% of the dataset to train these two layers. To avoid the overfitting problem, data augmentation and dynamic learning rate reduction have been used. With the proposed model, 98.981% test accuracy has been achieved. It is expected that this study may help to develop an efficient human-machine communication system for a deaf-mute society.
    Matched MeSH terms: Artificial Intelligence*
  15. Smart solutions for clean air: An AI-guided approach to sustainable industrial pollution control in coal-fired power plant
    Lim JY, Teng SY, How BS, Loy ACM, Heo S, Jansen J, et al.
    Environ Pollut, 2023 Oct 15;335:122335.
    PMID: 37558197 DOI: 10.1016/j.envpol.2023.122335
    Conventional fossil fuels are relied on heavily to meet the ever-increasing demand for energy required by human activities. However, their usage generates significant air pollutant emissions, such as NOx, SOx, and particulate matter. As a result, a complete air pollutant control system is necessary. However, the intensive operation of such systems is expected to cause deterioration and reduce their efficiency. Therefore, this study evaluates the current air pollutant control configuration of a coal-powered plant and proposes an upgraded system. Using a year-long dataset of air pollutants collected at 30-min intervals from the plant's telemonitoring system, untreated flue gas was reconstructed with a variational autoencoder. Subsequently, a superstructure model with various technology options for treating NOx, SOx, and particulate matter was developed. The most sustainable configuration, which included reburning, desulfurization with seawater, and dry electrostatic precipitator, was identified using an artificial intelligence (AI) model to meet economic, environmental, and reliability targets. Finally, the proposed system was evaluated using a Monte Carlo simulation to assess various scenarios with tightened discharge limits. The untreated flue gas was then evaluated using the most sustainable air pollutant control configuration, which demonstrated a total annual cost, environmental quality index, and reliability indices of 44.1 × 106 USD/year, 0.67, and 0.87, respectively.
    Matched MeSH terms: Artificial Intelligence
  16. Relations between creativity and intelligence among Malaysian pupils
    Yong LM
    Percept Mot Skills, 1994 Oct;79(2):739-42.
    PMID: 7870496
    This paper describes an empirical study of the relation between creativity and intelligence of 397 Malaysian secondary school pupils. The Torrance Tests of Creative Thinking, Figural Form A and Verbal Form A (Malay Language version), and the Cattell Culture Fair Intelligence Test were administered to 181 boys and 216 girls from five secondary schools located in the urban and suburban areas of Kuala Lumpur and Petaling Jaya in Malaysia. Analysis indicated that scores on verbal creativity were related to intelligence while those on figural creativity were not.
    Matched MeSH terms: Intelligence*
  17. Prospects and Challenges of MXenes as Emerging Sensing Materials for Flexible and Wearable Breath-Based Biomarker Diagnosis
    Hermawan A, Amrillah T, Riapanitra A, Ong WJ, Yin S
    Adv Healthc Mater, 2021 10;10(20):e2100970.
    PMID: 34318999 DOI: 10.1002/adhm.202100970
    A fully integrated, flexible, and functional sensing device for exhaled breath analysis drastically transforms conventional medical diagnosis to non-invasive, low-cost, real-time, and personalized health care. 2D materials based on MXenes offer multiple advantages for accurately detecting various breath biomarkers compared to conventional semiconducting oxides. High surface sensitivity, large surface-to-weight ratio, room temperature detection, and easy-to-assemble structures are vital parameters for such sensing devices in which MXenes have demonstrated all these properties both experimentally and theoretically. So far, MXenes-based flexible sensor is successfully fabricated at a lab-scale and is predicted to be translated into clinical practice within the next few years. This review presents a potential application of MXenes as emerging materials for flexible and wearable sensor devices. The biomarkers from exhaled breath are described first, with emphasis on metabolic processes and diseases indicated by abnormal biomarkers. Then, biomarkers sensing performances provided by MXenes families and the enhancement strategies are discussed. The method of fabrications toward MXenes integration into various flexible substrates is summarized. Finally, the fundamental challenges and prospects, including portable integration with Internet-of-Thing (IoT) and Artificial Intelligence (AI), are addressed to realize marketization.
    Matched MeSH terms: Artificial Intelligence
  18. Fulltext Design and implementation of adolescent health Latin dance teaching system under artificial intelligence technology
    Liu X, Soh KG, Dev Omar Dev R, Li W, Yi Q
    PLoS One, 2023;18(11):e0293313.
    PMID: 37917594 DOI: 10.1371/journal.pone.0293313
    Since various dance teaching systems have attracted much attention with the development of Artificial Intelligence (AI) technology, this paper improves the recognition performance of Latin dance teaching systems by optimizing the action recognition model. Firstly, the object detection and action recognition technology under the current AI technology is analyzed, and the Two-stage object detection algorithm and One-stage object detection algorithm are evaluated. Secondly, the technologies and functions contained in the adolescent health Latin dance teaching system are described, including image acquisition, feature extraction, object detection, and action recognition. Finally, the action recognition algorithm is optimized based on object detection, and the rationality and feasibility of the proposed algorithm are verified by experiments. The experimental results show that the optimization algorithm can search the optimal feature subset after five iterations on Undefine Classes of 101 (UCF101) dataset, but it needs seven iterations on Human Motion Database 51 (HMDB51) dataset. Meanwhile, when using support vector machine classifier, the optimization algorithm can achieve the highest accuracy of motion recognition. Regressive Function, Multinomial Naive Bayes and Gaussian Naive Bayes Algorithms have lower prediction delay, as low as 0.01s. Therefore, this paper has certain reference significance for the design and implementation of adolescent health Latin dance teaching system.
    Matched MeSH terms: Artificial Intelligence*
  19. Fulltext Opportunities and challenges of 5G network technology toward precision medicine
    Kang CC, Lee TY, Lim WF, Yeo WWY
    Clin Transl Sci, 2023 Nov;16(11):2078-2094.
    PMID: 37702288 DOI: 10.1111/cts.13640
    Moving away from traditional "one-size-fits-all" treatment to precision-based medicine has tremendously improved disease prognosis, accuracy of diagnosis, disease progression prediction, and targeted-treatment. The current cutting-edge of 5G network technology is enabling a growing trend in precision medicine to extend its utility and value to the smart healthcare system. The 5G network technology will bring together big data, artificial intelligence, and machine learning to provide essential levels of connectivity to enable a new health ecosystem toward precision medicine. In the 5G-enabled health ecosystem, its applications involve predictive and preventative measurements which enable advances in patient personalization. This review aims to discuss the opportunities, challenges, and prospects posed to 5G network technology in moving forward to deliver personalized treatments and patient-centric care via a precision medicine approach.
    Matched MeSH terms: Artificial Intelligence*
  20. Advancement in biosensor: "Telediagnosis" and "remote digital imaging"
    Gopinath SCB, Ismail ZH, Shapiai MI, Yasin MNM
    Biotechnol Appl Biochem, 2021 May 19.
    PMID: 34009645 DOI: 10.1002/bab.2196
    Current developments in sensors and actuators are heralding a new era to facilitate things to happen effortlessly and efficiently with proper communication. On the other hand, Internet of Things (IoT) has been boomed up with er potential and occupies a wide range of disciplines. This study has choreographed to design of an algorithm and a smart data-processing scheme to implement the obtained data from the sensing system to transmit to the receivers. Technically, it is called "telediagnosis" and "remote digital monitoring," a revolution in the field of medicine and artificial intelligence. For the proof of concept, an algorithmic approach has been implemented for telediagnosis with one of the degenerative diseases, that is, Parkinson's disease. Using the data acquired from an improved interdigitated electrode, sensing surface was evaluated with the attained sensitivity of 100 fM (n = 3), and the limit of detection was calculated with the linear regression value coefficient. By the designed algorithm and data processing with the assistance of IoT, further validation was performed and attested the coordination. This proven concept can be ideally used with all sensing strategies for immediate telemedicine by end-to-end communications.
    Matched MeSH terms: Artificial Intelligence
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