Generation of Finite Inductive, Pseudo Random, Binary Sequences

Fisher, Paul,Aljohani, Nawaf,Baek, Jinsuk Korea Information Processing Society 2017 Journal of information processing systems Vol.13 No.6

This paper introduces a new type of determining factor for Pseudo Random Strings (PRS). This classification depends upon a mathematical property called Finite Induction (FI). FI is similar to a Markov Model in that it presents a model of the sequence under consideration and determines the generating rules for this sequence. If these rules obey certain criteria, then we call the sequence generating these rules FI a PRS. We also consider the relationship of these kinds of PRS's to Good/deBruijn graphs and Linear Feedback Shift Registers (LFSR). We show that binary sequences from these special graphs have the FI property. We also show how such FI PRS's can be generated without consideration of the Hamiltonian cycles of the Good/deBruijn graphs. The FI PRS's also have maximum Shannon entropy, while sequences from LFSR's do not, nor are such sequences FI random.

A Novel Approach to COVID-19 Diagnosis Based on Mel Spectrogram Features and Artificial Intelligence Techniques

Alfaidi, Aseel,Alshahrani, Abdullah,Aljohani, Maha International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.9

COVID-19 has remained one of the most serious health crises in recent history, resulting in the tragic loss of lives and significant economic impacts on the entire world. The difficulty of controlling COVID-19 poses a threat to the global health sector. Considering that Artificial Intelligence (AI) has contributed to improving research methods and solving problems facing diverse fields of study, AI algorithms have also proven effective in disease detection and early diagnosis. Specifically, acoustic features offer a promising prospect for the early detection of respiratory diseases. Motivated by these observations, this study conceptualized a speech-based diagnostic model to aid in COVID-19 diagnosis. The proposed methodology uses speech signals from confirmed positive and negative cases of COVID-19 to extract features through the pre-trained Visual Geometry Group (VGG-16) model based on Mel spectrogram images. This is used in addition to the K-means algorithm that determines effective features, followed by a Genetic Algorithm-Support Vector Machine (GA-SVM) classifier to classify cases. The experimental findings indicate the proposed methodology's capability to classify COVID-19 and NOT COVID-19 of varying ages and speaking different languages, as demonstrated in the simulations. The proposed methodology depends on deep features, followed by the dimension reduction technique for features to detect COVID-19. As a result, it produces better and more consistent performance than handcrafted features used in previous studies.

Generation of Finite Inductive, Pseudo Random, Binary Sequences

Paul Fisher,Nawaf Aljohani,백진숙 한국정보처리학회 2017 Journal of information processing systems Vol.13 No.6

This paper introduces a new type of determining factor for Pseudo Random Strings (PRS). This classificationdepends upon a mathematical property called Finite Induction (FI). FI is similar to a Markov Model in that itpresents a model of the sequence under consideration and determines the generating rules for this sequence. If these rules obey certain criteria, then we call the sequence generating these rules FI a PRS. We also considerthe relationship of these kinds of PRS’s to Good/deBruijn graphs and Linear Feedback Shift Registers (LFSR). We show that binary sequences from these special graphs have the FI property. We also show how such FIPRS’s can be generated without consideration of the Hamiltonian cycles of the Good/deBruijn graphs. The FIPRS’s also have maximum Shannon entropy, while sequences from LFSR’s do not, nor are such sequences FIrandom.

A RANDOM GENERALIZED NONLINEAR IMPLICIT VARIATIONAL-LIKE INCLUSION WITH RANDOM FUZZY MAPPINGS

F. A. Khan,A. S. Aljohani,M. G. Alshehri,J. Ali 경남대학교 기초과학연구소 2021 Nonlinear Functional Analysis and Applications Vol.26 No.4

In this paper, we introduce and study a new class of random generalized nonlinear implicit variational-like inclusion with random fuzzy mappings in a real separable Hilbert space and give its fixed point formulation. Using the fixed point formulation and the proximal mapping technique for strongly maximal monotone mapping, we suggest and analyze a random iterative scheme for finding the approximate solution of this class of inclusion. Further, we prove the existence of solution and discuss the convergence analysis of iterative scheme of this class of inclusion. Our results in this paper improve and generalize several known results in the literature.

ITERATIVE ALGORITHM FOR RANDOM GENERALIZED NONLINEAR MIXED VARIATIONAL INCLUSIONS WITH RANDOM FUZZY MAPPINGS

Faizan Ahmad Khan,Eid Musallam Aljohani,Javid Ali 경남대학교 기초과학연구소 2022 Nonlinear Functional Analysis and Applications Vol.27 No.4

In this paper, we consider a class of random generalized nonlinear mixed variational inclusions with random fuzzy mappings and random relaxed cocoercive mappings inreal Hilbert spaces. We suggest and analyze an iterative algorithm for finding the approximate solution of this class of inclusions. Further, we discuss the convergence analysis ofthe iterative algorithm under some appropriate conditions. Our results can be viewed as a refinement and improvement of some known results in the literature.

Deep recurrent neural networks with word embeddings for Urdu named entity recognition

Khan, Wahab,Daud, Ali,Alotaibi, Fahd,Aljohani, Naif,Arafat, Sachi Electronics and Telecommunications Research Instit 2020 ETRI Journal Vol.42 No.1

Named entity recognition (NER) continues to be an important task in natural language processing because it is featured as a subtask and/or subproblem in information extraction and machine translation. In Urdu language processing, it is a very difficult task. This paper proposes various deep recurrent neural network (DRNN) learning models with word embedding. Experimental results demonstrate that they improve upon current state-of-the-art NER approaches for Urdu. The DRRN models evaluated include forward and bidirectional extensions of the long short-term memory and back propagation through time approaches. The proposed models consider both language-dependent features, such as part-of-speech tags, and language-independent features, such as the "context windows" of words. The effectiveness of the DRNN models with word embedding for NER in Urdu is demonstrated using three datasets. The results reveal that the proposed approach significantly outperforms previous conditional random field and artificial neural network approaches. The best f-measure values achieved on the three benchmark datasets using the proposed deep learning approaches are 81.1%, 79.94%, and 63.21%, respectively.

Diagnostic approach and use of CTPA in patients with suspected pulmonary embolism in an emergency department in Saudi Arabia

Feras Almarshad,Ali Alaklabi,Abdulrahman Al Raizah,Yousof AlZahrani,Somaya Awad Aljohani,Rawaby Khalid AlShammari,Al-zahraa Saleh Al-mahlawi,Abdulaziz Abdullah Alahmary,Mosaad Almegren,Dushad Ram 대한혈액학회 2023 Blood Research Vol.58 No.1

Background In patients with suspected pulmonary embolism (PE), the literature suggests the overuse of computerized tomography pulmonary angiography (CTPA) and underuse of clinical decision rules before imaging request. This study determined the potential for avoidable CTPA using the modified Wells score (mWS) and D-dimer assay in patients with suspected PE. Methods This hospital-based retrospective study analyzed the clinical data of 661 consecutive patients with suspected PE who underwent CTPA in the emergency department of a tertiary hospital for the use of a clinical prediction rule (mWS) and D-dimer assay. The score was calculated retrospectively from the available data in the files of patients who did not have a documented clinical prediction rule. Overuse (avoidable) CTPA was defined as D-dimer negativity and PE unlikely for this study. Results Of 661 patients’ data examined, clinical prediction rules were documented in 15 (2.3%). In total, 422 patients (63.8%) had required information on modified Wells criteria and D-dimer assays and were included for further analysis. PE on CTPA was present in 22 (5.21%) of PE unlikely (mWS ≤4) and 1 (0.24%) of D-dimer negative patients. Thirty patients (7.11%) met the avoidable CTPA (DD negative+PE unlikely) criteria, and it was significantly associated with dyspnea. The value of sensitivity of avoidable CTPA was 100%, whereas the positive predictive value was 90.3%. Conclusion Underutilization of clinical prediction rules before prescribing CTPA is common in emergency departments. Therefore, a mandatory policy should be implemented regarding the evaluation of avoidable CTPA imaging to reduce CTPA overuse.