Artificial Intelligence in Hybrid Vehicle Transmission Control - Literature Review and Research Methodology

2021 ◽  
Author(s):  
Florian Schuchter ◽  
Katharina Bause ◽  
Albert Albers
Keyword(s):  
Artificial Intelligence ◽  
Literature Review ◽  
Research Methodology ◽  
Hybrid Vehicle ◽  
Transmission Control

Metodologi Pengeluaran Fatwa di Jawatankuasa Fatwa Negeri Johor

2018 ◽  
Vol 13 (1) ◽  
pp. 70-88
Author(s):  
Mohd Faez Mohd Shah ◽  
Norhidayah Pauzi
Keyword(s):  
Literature Review ◽  
Research Methods ◽  
Research Methodology ◽  
Research Method ◽  
Islamic Law ◽  
Field Research ◽  
The State ◽  
Original Source ◽  
Interview Methods ◽  
The Right

In the discipline of Islamic law research, strong proofing and clear Istinbat method are key pillars in the construction of Islamic law based on the application of the science of usul al-fiqh and maqasid al-shari'ah. However, what happens at the state of Johor’s fatwa institution is the opposite. The fatwa research methods applied by the Fatwa Committee of Johor in resolving current fatwa issues is not based on the right and true discipline of Islamic law research. In fact, current inputs related to fatwa issues are not explicitly stated in the method of determining the law either in the form of reality or scientifically verified. Therefore, this paper will discuss the fatwa procedures undertaken by the Fatwa Committee of Johor based on the methods applied in resolving current issues. The research methodology adopted is library and interview methods. This study shows that fatwa management and production in the state of Johor is placed under the jurisdiction of the Mufti of Johor’s Department. The methods adopted by the Fatwa Committee of Johor covers two methods, namely: internal research methods including literature review through the application of original source and proofs based on syarak. Second: field research method that includes an external review or going to the location of study such as conducting observation, questionnaires and interviews including referrals to specialists of different fields. Maslahah and mafsdah consideration are also implemented by the Fatwa Committee in every fatwa decision based on the standard that meets the interests of maqasid al-shari'ah. Keywords: Metode, fatwa, istinbat, usul al-fiqh, maqasid al-shari’ah ABSTRAK Dalam disiplin penyelidikan hukum Islam, kekuatan pendalilan dan kaedah istinbat yang jelas merupakan tunggak utama dalam pembinaan hukum Islam berasaskan kepada aplikasi ilmu usul al-fiqh dan maqasid al-shari’ah. Namun begitu, apa yang berlaku di institusi fatwa negeri Johor adalah sebaliknya. Kaedah penyelidikan fatwa yang diaplikasi oleh Jawatankuasa Fatwa Negeri Johor dalam menyelesaikan isu fatwa semasa tidak berasaskan kepada disiplin penyelidikan hukum Islam yang tepat dan sebenar. Malahan input-input semasa yang berkaitan dengan isu fatwa juga tidak dinyatakan secara jelas dalam kaedah penentuan hukum sama ada dalam bentuk realiti yang berlaku atau pembuktian secara saintifik. Justeru, kertas kerja ini akan membincangkan prosedur fatwa Jawatankuasa Fatwa Negeri Johor berdasarkan metode-metode yang diaplikasi dalam menyelesaikan isu-isu yang bersifat semasa. Metodologi kajian yang digunakan dalam kajian ini adalah melalui metode perpustakaan dan metode lapangan. Hasil kajian menunjukkan bahawa pengurusan dan pengeluaran fatwa di negeri Johor hanya terletak di bawah bidang kuasa Jabatan Mufti Johor. Metode fatwa yang diamalkan oleh Jawatankuasa Fatwa Negeri Johor merangkumi dua metode iaitu pertama, kaedah penyelidikan dalaman yang merangkumi kajian kepustakaan menerusi pengaplikasian dari sumber asas dan dalil-dalil syarak. Kedua, kaedah penyelidikan lapangan yang meliputi kajian luaran atau turun ke lokasi kajian seperti observasi, soal selidik dan temubual dan rujukan kepada pakar dalam bidang yang berlainan. Pertimbangan maslahah dan mafsdah juga dimplementasikan oleh Jawatankuasa Fatwa dalam setiap keputusan fatwanya berasaskan standard yang menepati kepentingan maqasid al-shari’ah. Kata kunci: Metode, fatwa, istinbat, usul al-fiqh, maqasid al-shari’ah

Perceived Effectiveness of Artificial Intelligence based Decision Support System in a Clinical Setting: A Systematic Literature Review (Preprint)

2020 ◽  
Author(s):  
Avishek Choudhury
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Support ◽  
Literature Review ◽  
Decision Support System ◽  
Support System ◽  
Clinical Setting ◽  
Clinical Decision Making ◽  
Theoretical Perspective ◽  
Care Providers

UNSTRUCTURED Objective: The potential benefits of artificial intelligence based decision support system (AI-DSS) from a theoretical perspective are well documented and perceived by researchers but there is a lack of evidence showing its influence on routine clinical practice and how its perceived by care providers. Since the effectiveness of AI systems depends on data quality, implementation, and interpretation. The purpose of this literature review is to analyze the effectiveness of AI-DSS in clinical setting and understand its influence on clinician’s decision making outcome. Materials and Methods: This review protocol follows the Preferred Reporting Items for Systematic Reviews and Meta- Analyses reporting guidelines. Literature will be identified using a multi-database search strategy developed in consultation with a librarian. The proposed screening process consists of a title and abstract scan, followed by a full-text review by two reviewers to determine the eligibility of articles. Studies outlining application of AI based decision support system in a clinical setting and its impact on clinician’s decision making, will be included. A tabular synthesis of the general study details will be provided, as well as a narrative synthesis of the extracted data, organised into themes. Studies solely reporting AI accuracy an but not implemented in a clinical setting to measure its influence on clinical decision making were excluded from further review. Results: We identified 8 eligible studies that implemented AI-DSS in a clinical setting to facilitate decisions concerning prostate cancer, post traumatic stress disorder, cardiac ailment, back pain, and others. Five (62.50%) out of 8 studies reported positive outcome of AI-DSS. Conclusion: The systematic review indicated that AI-enabled decision support systems, when implemented in a clinical setting and used by clinicians might not ensure enhanced decision making. However, there are very limited studies to confirm the claim that AI based decision support system can uplift clinicians decision making abilities.

scholarly journals Human-Centered Artificial Intelligence for Designing Accessible Cultural Heritage

2021 ◽  
Vol 11 (2) ◽  
pp. 870
Author(s):  
Galena Pisoni ◽  
Natalia Díaz-Rodríguez ◽  
Hannie Gijlers ◽  
Linda Tonolli
Keyword(s):  
Artificial Intelligence ◽  
Literature Review ◽  
Cultural Heritage ◽  
Conceptual Framework ◽  
Interaction Design ◽  
Participatory Design ◽  
Future Directions ◽  
Heritage Sites ◽  
Review Analysis

This paper reviews the literature concerning technology used for creating and delivering accessible museum and cultural heritage sites experiences. It highlights the importance of the delivery suited for everyone from different areas of expertise, namely interaction design, pedagogical and participatory design, and it presents how recent and future artificial intelligence (AI) developments can be used for this aim, i.e.,improving and widening online and in situ accessibility. From the literature review analysis, we articulate a conceptual framework that incorporates key elements that constitute museum and cultural heritage online experiences and how these elements are related to each other. Concrete opportunities for future directions empirical research for accessibility of cultural heritage contents are suggested and further discussed.

scholarly journals The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review

Diagnostics ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1317
Author(s):  
Maria Elena Laino ◽  
Angela Ammirabile ◽  
Alessandro Posa ◽  
Pierandrea Cancian ◽  
Sherif Shalaby ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Literature Review ◽  
Diagnostic Imaging ◽  
Clinical Work ◽  
Rapid Expansion ◽  
Chest Imaging ◽  
Imaging Data ◽  
Prognostic Information ◽  
Quantitative Ct ◽  
Work Up

Diagnostic imaging is regarded as fundamental in the clinical work-up of patients with a suspected or confirmed COVID-19 infection. Recent progress has been made in diagnostic imaging with the integration of artificial intelligence (AI) and machine learning (ML) algorisms leading to an increase in the accuracy of exam interpretation and to the extraction of prognostic information useful in the decision-making process. Considering the ever expanding imaging data generated amid this pandemic, COVID-19 has catalyzed the rapid expansion in the application of AI to combat disease. In this context, many recent studies have explored the role of AI in each of the presumed applications for COVID-19 infection chest imaging, suggesting that implementing AI applications for chest imaging can be a great asset for fast and precise disease screening, identification and characterization. However, various biases should be overcome in the development of further ML-based algorithms to give them sufficient robustness and reproducibility for their integration into clinical practice. As a result, in this literature review, we will focus on the application of AI in chest imaging, in particular, deep learning, radiomics and advanced imaging as quantitative CT.

scholarly journals Applications of artificial intelligence to improve patient flow on mental health inpatient units - Narrative literature review

Heliyon ◽  
2021 ◽  
Vol 7 (4) ◽  
pp. e06626
Author(s):  
Paulina Cecula ◽  
Jiakun Yu ◽  
Fatema Mustansir Dawoodbhoy ◽  
Jack Delaney ◽  
Joseph Tan ◽  
...  
Keyword(s):  
Mental Health ◽  
Artificial Intelligence ◽  
Literature Review ◽  
Patient Flow ◽  
Narrative Literature ◽  
Narrative Literature Review ◽  
Inpatient Units ◽  
Improve Patient

How and where is artificial intelligence in the public sector going? A literature review and research agenda

2019 ◽  
Vol 36 (4) ◽  
pp. 101392 ◽  
Author(s):  
Weslei Gomes de Sousa ◽  
Elis Regina Pereira de Melo ◽  
Paulo Henrique De Souza Bermejo ◽  
Rafael Araújo Sousa Farias ◽  
Adalmir Oliveira Gomes
Keyword(s):  
Artificial Intelligence ◽  
Public Sector ◽  
Literature Review ◽  
Research Agenda ◽  
The Public

NIMG-46. SYSTEMATIC LITERATURE REVIEW OF ARTIFICIAL INTELLIGENCE ALGORITHMS USING PRE-THERAPY MR IMAGING FOR GLIOMA MOLECULAR SUBTYPE CLASSIFICATION

2021 ◽  
Vol 23 (Supplement_6) ◽  
pp. vi139-vi139
Author(s):  
Jan Lost ◽  
Tej Verma ◽  
Niklas Tillmanns ◽  
W R Brim ◽  
Harry Subramanian ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Literature Review ◽  
Prediction Accuracy ◽  
Molecular Subtypes ◽  
Molecular Subtype ◽  
Tumor Resection ◽  
Support Vector ◽  
Hospital Data

Abstract PURPOSE Identifying molecular subtypes in gliomas has prognostic and therapeutic value, traditionally after invasive neurosurgical tumor resection or biopsy. Recent advances using artificial intelligence (AI) show promise in using pre-therapy imaging for predicting molecular subtype. We performed a systematic review of recent literature on AI methods used to predict molecular subtypes of gliomas. METHODS Literature review conforming to PRSIMA guidelines was performed for publications prior to February 2021 using 4 databases: Ovid Embase, Ovid MEDLINE, Cochrane trials (CENTRAL), and Web of Science core-collection. Keywords included: artificial intelligence, machine learning, deep learning, radiomics, magnetic resonance imaging, glioma, and glioblastoma. Non-machine learning and non-human studies were excluded. Screening was performed using Covidence software. Bias analysis was done using TRIPOD guidelines. RESULTS 11,727 abstracts were retrieved. After applying initial screening exclusion criteria, 1,135 full text reviews were performed, with 82 papers remaining for data extraction. 57% used retrospective single center hospital data, 31.6% used TCIA and BRATS, and 11.4% analyzed multicenter hospital data. An average of 146 patients (range 34-462 patients) were included. Algorithms predicting IDH status comprised 51.8% of studies, MGMT 18.1%, and 1p19q 6.0%. Machine learning methods were used in 71.4%, deep learning in 27.4%, and 1.2% directly compared both methods. The most common algorithm for machine learning were support vector machine (43.3%), and for deep learning convolutional neural network (68.4%). Mean prediction accuracy was 76.6%. CONCLUSION Machine learning is the predominant method for image-based prediction of glioma molecular subtypes. Major limitations include limited datasets (60.2% with under 150 patients) and thus limited generalizability of findings. We recommend using larger annotated datasets for AI network training and testing in order to create more robust AI algorithms, which will provide better prediction accuracy to real world clinical datasets and provide tools that can be translated to clinical practice.

Sign in / Sign up

Export Citation Format

Share Document