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  1. Book: Handbuch der homöopathischen Arzneibeziehungen

    Rehman, Abdur

    2007  

    Title translation Encyclopedia of remedy relationships in homoeopathy
    Author's details Abdur Rehman
    Keywords Homöopathisches Arzneimittel ; Arzneimittelwechselwirkung
    Subject Arzneimittel ; Arzneimittelinterferenz ; Arzneimittelinteraktion ; Interaktion ; Medikamente ; Homöopathisches Präparat
    Language German
    Size XXVIII, 353 S.
    Edition 3., überarb. Aufl.
    Publisher Haug
    Publishing place Stuttgart
    Publishing country Germany
    Document type Book
    HBZ-ID HT015132513
    ISBN 978-3-8304-7258-2 ; 3-8304-7258-7
    Database Catalogue ZB MED Medicine, Health

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    In stock of ZB MED Cologne/Königswinter

    Shelf markLoan statusLocation
    2007 A 3092 order for loan Magazin

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  2. Article ; Online: Light microscopic iris classification using ensemble multi-class support vector machine.

    Rehman, Amjad

    Microscopy research and technique

    2021  Volume 84, Issue 5, Page(s) 982–991

    Abstract: Similar to other biometric systems such as fingerprint, face, DNA, iris classification could assist law enforcement agencies in identifying humans. Iris classification technology helps law-enforcement agencies to recognize humans by matching their iris ... ...

    Abstract Similar to other biometric systems such as fingerprint, face, DNA, iris classification could assist law enforcement agencies in identifying humans. Iris classification technology helps law-enforcement agencies to recognize humans by matching their iris with iris data sets. However, iris classification is challenging in the real environment due to its invertible and complex texture variations in the human iris. Accordingly, this article presents an improved Oriented FAST and Rotated BRIEF with Bag-of-Words model to extract distinct and robust features from the iris image, followed by ensemble multi-class-SVM to classify iris. The proposed methodology consists of four main steps; first, iris image normalization and enhancement; second, localizing iris region; third, iris feature extraction; finally, iris classification using ensemble multi-class support vector machine. For preprocessing of input images, histogram equalization, Gaussian mask and median filters are applied. The proposed technique is tested on two benchmark databases, that is, CASIA-v1 and iris image database, and achieved higher accuracy than other existing techniques reported in state of the art.
    MeSH term(s) Algorithms ; Humans ; Iris/diagnostic imaging ; Microscopy ; Support Vector Machine
    Language English
    Publishing date 2021-01-12
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1099714-3
    ISSN 1097-0029 ; 1059-910X
    ISSN (online) 1097-0029
    ISSN 1059-910X
    DOI 10.1002/jemt.23659
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

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    Jg. 1995 - 2021: Lesesall (1.OG)
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  3. Article ; Online: Orange Alert.

    Rehman, Abdul

    Academic emergency medicine : official journal of the Society for Academic Emergency Medicine

    2020  Volume 27, Issue 9, Page(s) 937

    Language English
    Publishing date 2020-02-14
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1329813-6
    ISSN 1553-2712 ; 1069-6563
    ISSN (online) 1553-2712
    ISSN 1069-6563
    DOI 10.1111/acem.13925
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 4479: Show issues Location:
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  4. Article ; Online: Aging and Adiposity-Focus on Biological Females at Midlife and Beyond.

    Rehman, Amna / Lathief, Sanam / Charoenngam, Nipith / Pal, Lubna

    International journal of molecular sciences

    2024  Volume 25, Issue 5

    Abstract: Menopause is a physiological phase of life of aging women, and more than 1 billion women worldwide will be in menopause by 2025. The processes of global senescence parallel stages of reproductive aging and occur alongside aging-related changes in the ... ...

    Abstract Menopause is a physiological phase of life of aging women, and more than 1 billion women worldwide will be in menopause by 2025. The processes of global senescence parallel stages of reproductive aging and occur alongside aging-related changes in the body. Alterations in the endocrine pathways accompany and often predate the physiologic changes of aging, and interactions of these processes are increasingly being recognized as contributory to the progression of senescence. Our goal for this review is to examine, in aging women, the complex interplay between the endocrinology of menopause transition and post-menopause, and the metabolic transition, the hallmark being an increasing tendency towards central adiposity that begins in tandem with reproductive aging and is often exacerbated post menopause. For the purpose of this review, our choice of the terms 'female' and 'woman' refer to genetic females.
    MeSH term(s) Female ; Humans ; Adiposity ; Aging/metabolism ; Menopause/physiology ; Postmenopause ; Reproduction ; Obesity
    Language English
    Publishing date 2024-03-04
    Publishing country Switzerland
    Document type Journal Article ; Review
    ZDB-ID 2019364-6
    ISSN 1422-0067 ; 1422-0067 ; 1661-6596
    ISSN (online) 1422-0067
    ISSN 1422-0067 ; 1661-6596
    DOI 10.3390/ijms25052972
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  5. Article ; Online: DEW: A wavelet approach of rare sound event detection.

    Gul, Sania / Khan, Muhammad Salman / Ur-Rehman, Ata

    PloS one

    2024  Volume 19, Issue 3, Page(s) e0300444

    Abstract: This paper presents a novel sound event detection (SED) system for rare events occurring in an open environment. Wavelet multiresolution analysis (MRA) is used to decompose the input audio clip of 30 seconds into five levels. Wavelet denoising is then ... ...

    Abstract This paper presents a novel sound event detection (SED) system for rare events occurring in an open environment. Wavelet multiresolution analysis (MRA) is used to decompose the input audio clip of 30 seconds into five levels. Wavelet denoising is then applied on the third and fifth levels of MRA to filter out the background. Significant transitions, which may represent the onset of a rare event, are then estimated in these two levels by combining the peak-finding algorithm with the K-medoids clustering algorithm. The small portions of one-second duration, called 'chunks' are cropped from the input audio signal corresponding to the estimated locations of the significant transitions. Features from these chunks are extracted by the wavelet scattering network (WSN) and are given as input to a support vector machine (SVM) classifier, which classifies them. The proposed SED framework produces an error rate comparable to the SED systems based on convolutional neural network (CNN) architecture. Also, the proposed algorithm is computationally efficient and lightweight as compared to deep learning models, as it has no learnable parameter. It requires only a single epoch of training, which is 5, 10, 200, and 600 times lesser than the models based on CNNs and deep neural networks (DNNs), CNN with long short-term memory (LSTM) network, convolutional recurrent neural network (CRNN), and CNN respectively. The proposed model neither requires concatenation with previous frames for anomaly detection nor any additional training data creation needed for other comparative deep learning models. It needs to check almost 360 times fewer chunks for the presence of rare events than the other baseline systems used for comparison in this paper. All these characteristics make the proposed system suitable for real-time applications on resource-limited devices.
    MeSH term(s) Neural Networks, Computer ; Algorithms ; Wavelet Analysis ; Memory ; Support Vector Machine
    Language English
    Publishing date 2024-03-28
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0300444
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: The Revival of Essay-Type Questions in Medical Education: Harnessing Artificial Intelligence and Machine Learning.

    Shamim, Muhammad Shahid / Zaidi, Syed Jaffar Abbas / Rehman, Abdur

    Journal of the College of Physicians and Surgeons--Pakistan : JCPSP

    2024  Volume 34, Issue 5, Page(s) 595–599

    Abstract: Objective: To analyse and compare the assessment and grading of human-written and machine-written formative essays.: Study design: Quasi-experimental, qualitative cross-sectional study. Place and Duration of the Study: Department of Science of Dental ...

    Abstract Objective: To analyse and compare the assessment and grading of human-written and machine-written formative essays.
    Study design: Quasi-experimental, qualitative cross-sectional study. Place and Duration of the Study: Department of Science of Dental Materials, Hamdard College of Medicine & Dentistry, Hamdard University, Karachi, from February to April 2023.
    Methodology: Ten short formative essays of final-year dental students were manually assessed and graded. These essays were then graded using ChatGPT version 3.5. The chatbot responses and prompts were recorded and matched with manually graded essays. Qualitative analysis of the chatbot responses was then performed.
    Results: Four different prompts were given to the artificial intelligence (AI) driven platform of ChatGPT to grade the summative essays. These were the chatbot's initial responses without grading, the chatbot's response to grading against criteria, the chatbot's response to criteria-wise grading, and the chatbot's response to questions for the difference in grading. Based on the results, four innovative ways of using AI and machine learning (ML) have been proposed for medical educators: Automated grading, content analysis, plagiarism detection, and formative assessment. ChatGPT provided a comprehensive report with feedback on writing skills, as opposed to manual grading of essays.
    Conclusion: The chatbot's responses were fascinating and thought-provoking. AI and ML technologies can potentially supplement human grading in the assessment of essays. Medical educators need to embrace AI and ML technology to enhance the standards and quality of medical education, particularly when assessing long and short essay-type questions. Further empirical research and evaluation are needed to confirm their effectiveness.
    Key words: Machine learning, Artificial intelligence, Essays, ChatGPT, Formative assessment.
    MeSH term(s) Humans ; Artificial Intelligence ; Cross-Sectional Studies ; Machine Learning ; Educational Measurement/methods ; Pakistan ; Education, Medical/methods ; Students, Dental/psychology ; Writing ; Qualitative Research ; Education, Dental/methods
    Language English
    Publishing date 2024-05-09
    Publishing country Pakistan
    Document type Journal Article
    ZDB-ID 2276646-7
    ISSN 1681-7168 ; 1022-386X
    ISSN (online) 1681-7168
    ISSN 1022-386X
    DOI 10.29271/jcpsp.2024.05.595
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 6532: Show issues Location:
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  7. Article ; Online: Optimised stacked machine learning algorithms for genomics and genetics disorder detection in the healthcare industry.

    Rehman, Amjad / Mujahid, Muhammad / Saba, Tanzila / Jeon, Gwanggil

    Functional & integrative genomics

    2024  Volume 24, Issue 1, Page(s) 23

    Abstract: With recent advances in precision medicine and healthcare computing, there is an enormous demand for developing machine learning algorithms in genomics to enhance the rapid analysis of disease disorders. Technological advancement in genomics and imaging ... ...

    Abstract With recent advances in precision medicine and healthcare computing, there is an enormous demand for developing machine learning algorithms in genomics to enhance the rapid analysis of disease disorders. Technological advancement in genomics and imaging provides clinicians with enormous amounts of data, but prediction is still mostly subjective, resulting in problematic medical treatment. Machine learning is being employed in several domains of the healthcare sector, encompassing clinical research, early disease identification, and medicinal innovation with a historical perspective. The main objective of this study is to detect patients who, based on several medical standards, are more susceptible to having a genetic disorder. A genetic disease prediction algorithm was employed, leveraging the patient's health history to evaluate the probability of diagnosing a genetic disorder. We developed a computationally efficient machine learning approach to predict the overall lifespan of patients with a genomics disorder and to classify and predict patients with a genetic disease. The SVM, RF, and ETC are stacked using two-layer meta-estimators to develop the proposed model. The first layer comprises all the baseline models employed to predict the outcomes based on the dataset. The second layer comprises a component known as a meta-classifier. Results from the experiment indicate that the model achieved an accuracy of 90.45% and a recall score of 90.19%. The area under the curve (AUC) for mitochondrial diseases is 98.1%; for multifactorial diseases, it is 97.5%; and for single-gene inheritance, it is 98.8%. The proposed approach presents a novel method for predicting patient prognosis in a manner that is unbiased, accurate, and comprehensive. The proposed approach outperforms human professionals using the current clinical standard for genetic disease classification in terms of identification accuracy. The implementation of stacked will significantly improve the field of biomedical research by improving the anticipation of genetic diseases.
    MeSH term(s) Humans ; Health Care Sector ; Machine Learning ; Algorithms ; Databases, Genetic ; Genomics
    Language English
    Publishing date 2024-02-02
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 2014670-X
    ISSN 1438-7948 ; 1438-793X
    ISSN (online) 1438-7948
    ISSN 1438-793X
    DOI 10.1007/s10142-024-01289-z
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 5230: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
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  8. Book: Homöopathische Behandlung symptomarmer Fälle

    Rehman, Abdur

    Therapiebuch von A - Z nach exakten Quellen der homöopathischen Weltliteratur

    2001  

    Author's details Abdur Rehman
    Keywords Symptom ; Homöopathisches Arzneimittel
    Subject Homöopathisches Präparat ; Krankheitssymptom ; Krankheit ; Krankheitszeichen ; Symptomatik ; Reallexikon ; Sachwörterbuch ; Sprachwörterbuch ; Vokabular ; Vokabularium ; Wörterbücher ; Dictionary
    Language German
    Size X, 164 S.
    Publisher Sonntag
    Publishing place Stuttgart
    Publishing country Germany
    Document type Book
    HBZ-ID HT013073196
    ISBN 3-87758-231-1 ; 978-3-87758-231-2
    Database Catalogue ZB MED Medicine, Health

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    In stock of ZB MED Cologne/Königswinter

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  9. Book: Handbuch der homöopathischen Arzneibeziehungen

    Rehman, Abdur

    2000  

    Title translation Encyclopedia of remedy relationships in homeopathy
    Author's details von Abdur Rehman
    Keywords Homöopathisches Arzneimittel ; Arzneimittelwechselwirkung
    Subject Arzneimittel ; Arzneimittelinterferenz ; Arzneimittelinteraktion ; Interaktion ; Medikamente ; Homöopathisches Präparat
    Language German
    Size 374 S.
    Publisher Haug
    Publishing place Heidelberg
    Publishing country Germany
    Document type Book
    Note Aus d. Engl. übers.
    HBZ-ID HT012747587
    ISBN 3-8304-7026-6 ; 978-3-8304-7026-7
    Database Catalogue ZB MED Medicine, Health

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    In stock of ZB MED Cologne/Königswinter

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  10. Book ; Online: Neural Computing for Online Arabic Handwriting Character Recognition using Hard Stroke Features Mining

    Rehman, Amjad

    2020  

    Abstract: Online Arabic cursive character recognition is still a big challenge due to the existing complexities including Arabic cursive script styles, writing speed, writer mood and so forth. Due to these unavoidable constraints, the accuracy of online Arabic ... ...

    Abstract Online Arabic cursive character recognition is still a big challenge due to the existing complexities including Arabic cursive script styles, writing speed, writer mood and so forth. Due to these unavoidable constraints, the accuracy of online Arabic character's recognition is still low and retain space for improvement. In this research, an enhanced method of detecting the desired critical points from vertical and horizontal direction-length of handwriting stroke features of online Arabic script recognition is proposed. Each extracted stroke feature divides every isolated character into some meaningful pattern known as tokens. A minimum feature set is extracted from these tokens for classification of characters using a multilayer perceptron with a back-propagation learning algorithm and modified sigmoid function-based activation function. In this work, two milestones are achieved; firstly, attain a fixed number of tokens, secondly, minimize the number of the most repetitive tokens. For experiments, handwritten Arabic characters are selected from the OHASD benchmark dataset to test and evaluate the proposed method. The proposed method achieves an average accuracy of 98.6% comparable in state of art character recognition techniques.

    Comment: 16 pages
    Keywords Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Computation and Language
    Subject code 006
    Publishing date 2020-05-02
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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