Smart Health Monitoring System (SHMS) An Enabling Technology for patient Care

Irfan Ali Kandhro,Asif Ali Wagan,Muhammad Abdul Aleem,Rasheeda Ali Hassan,Ali Abbas International Journal of Computer ScienceNetwork S 2024 International journal of computer science and netw Vol.24 No.3

Health Monitoring System is a sophisticating technology and another way to the normal/regular management of the health of the patient. This Health Monitoring Mobile Application is a contribution from our side to the public and to the overall health industry in Pakistan. With the help of Health mobile application, the users will be able to store their medical records, prescriptions and retrieve them later. The users can store and keep track of their vital readings (heart rate, blood pressure, fasting glucose, random glucose). The mobile application also shows hospitals that are nearby in case the user wants to avail of any medical help. An important feature of the application is the symptoms-based disease prediction, the user selects the symptoms which he has and then the application will name certain diseases that match those symptoms based on relevant algorithms. The major advances and issues have been discussed, and as well as potential tasks to health monitoring will be identified and evaluated.

Personality Traits and Common Psychiatric Conditions in Adult Patients with Acne Vulgaris

( Ali Irfan Gul ),( Emine Colgecen ) 대한피부과학회 2015 Annals of Dermatology Vol.27 No.1

Background: We believe that instances of neuroticism andcommon psychiatric disorders are higher in adults with acnevulgaris than the normal population. Objective: Instances ofacne in adults have been increasing in frequency in recentyears. The aim of this study was to investigate personalitytraits and common psychiatric conditions in patients withadult acne vulgaris. Methods: Patients who visited thedermatology outpatient clinic at Bozok University MedicalSchool with a complaint of acne and who volunteered for thisstudy were included. The Symptom Checklist 90-Revised(SCL 90-R) Global Symptom Index (GSI), somatization,depression, and anxiety subscales and the Eysenck PersonalityQuestionnaire-Revised Short Form (EPQ-RSF) were administeredto 40 patients who fulfilled the inclusion criteriabefore treatment. The results were compared with those of acontrol group. Results: Of the 40 patients included in thisstudy, 34 were female and 6 were male. The GSI and thesomatization, depression, and anxiety subscales of the SCL90-R were evaluated. Patients with adult acne had statisticallysignificant higher scores than the control group on allof these subscales. In addition, patients with adult acne hadstatistically significantly higher scores on the neuroticismsubscale of the EPQ-RSF. Conclusion: Our results show thatcommon psychiatric conditions are frequent in adult patientswith acne. More importantly, neurotic personality characteristicsare observed more frequently in these patients. These findings suggest that acne in adults is a disorder thathas both medical and psychosomatic characteristics andrequires a multi-disciplinary approach. (Ann Dermatol 27(1) 48∼52, 2015)

Enhancement in Isolation among Collinearly Placed Microstrip Patch Antenna Arrays

Irfan Ali, Tunio,Hernan, Dellamaggiora,Umair, Saeed,Ayaz Ahmed, Hoshu,Ghulam, Hussain International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.1

Strong surface waves among collinearly arranged patch antenna arrays pose unwanted inter element coupling particularly when high permittivity dielectric materials are used. In order to avert those waves, a novel Defected Ground Structure (DGS) is carved out systematically between two E-plane patch antenna elements. The introduced low profile μ shaped structure consequently improves impedance bandwidth and reflection coefficient by suppressing surface waves considerably. Parametric simulation results are analyzed and discussed.

Aggregation in Group Recommendations

Irfan Ali,Sang-Wook Kim 한국콘텐츠학회 2017 한국콘텐츠학회 ICCC 논문집 Vol.2017 No.12

On Evaluating the Accuracy of Trust-aware Recommendation Approaches

Irfan Ali,Sang-Wook Kim 한국콘텐츠학회 2017 한국콘텐츠학회 ICCC 논문집 Vol.2017 No.12

Resume Classification System using Natural Language Processing & Machine Learning Techniques

Irfan Ali,Nimra,Ghulam Mujtaba,Zahid Hussain Khand,Zafar Ali,Sajid Khan International Journal of Computer ScienceNetwork S 2024 International journal of computer science and netw Vol.24 No.7

The selection and recommendation of a suitable job applicant from the pool of thousands of applications are often daunting jobs for an employer. The recommendation and selection process significantly increases the workload of the concerned department of an employer. Thus, Resume Classification System using the Natural Language Processing (NLP) and Machine Learning (ML) techniques could automate this tedious process and ease the job of an employer. Moreover, the automation of this process can significantly expedite and transparent the applicants' selection process with mere human involvement. Nevertheless, various Machine Learning approaches have been proposed to develop Resume Classification Systems. However, this study presents an automated NLP and ML-based system that classifies the Resumes according to job categories with performance guarantees. This study employs various ML algorithms and NLP techniques to measure the accuracy of Resume Classification Systems and proposes a solution with better accuracy and reliability in different settings. To demonstrate the significance of NLP & ML techniques for processing & classification of Resumes, the extracted features were tested on nine machine learning models Support Vector Machine - SVM (Linear, SGD, SVC & NuSVC), Naïve Bayes (Bernoulli, Multinomial & Gaussian), K-Nearest Neighbor (KNN) and Logistic Regression (LR). The Term-Frequency Inverse Document (TF-IDF) feature representation scheme proven suitable for Resume Classification Task. The developed models were evaluated using F-Score_M, Recall_M, Precission_M, and overall Accuracy. The experimental results indicate that using the One-Vs-Rest-Classification strategy for this multi-class Resume Classification task, the SVM class of Machine Learning algorithms performed better on the study dataset with over 96% overall accuracy. The promising results suggest that NLP & ML techniques employed in this study could be used for the Resume Classification task.

Predictive Role of the Neutrophil Lymphocyte Ratio for Invasion with Gestational Trophoblastic Disease

Guzel, Ali Irfan,Kokanali, Mahmut Kuntay,Erkilinc, Selcuk,Topcu, Hasan Onur,Oz, Murat,Ozgu, Emre,Erkaya, Salim,Gungor, Tayfun Asian Pacific Journal of Cancer Prevention 2014 Asian Pacific journal of cancer prevention Vol.15 No.10

Purpose: The objective of this study was to assess the predictive role of the neutrophil/lymphocyte ratio (NLR) for invasion of gestational trophoblastic disease (GTD). Materials and Methods: A retrospective analysis was conducted on 127 women who were managed at our clinic for GTD. Of all patients, 8 showed invasion according to histological examination. The clinical parameters of patients with invasive GTD (Group 1; n=8) were compared with patients who showed no invasion (Group 2; n=119). All underwent a prior uterine evacuation and followed up by regular assessment of ${\beta}$-hCG titers. Results: Demographic and obstetric history and pre-evacuation hCG levels of the patients showed no statistically significantly difference between the groups (p>0.05). The mean gestational weeks (GW), size of the GTD and NLR levels were statistically significantly higher in the invasive GTD group (p<0.05). Correlations between invasion and gestational weeks, size of GTD, post-evacuation chemotherapy and NLR were evident. ROC curve analysis demonstrated that GW, size of GTD and NLR may be discriminative parameters in predicting invasion of GTD. Conclusions: To the best of our knowledge, this is the first study evaluating the predictive role of NLR in invasion of GTD. In conclusion, we think that pretreatment NLR can be used as a biomarker of invasion in GTD.

Performance Analysis and Comparison for Various Excitation Source Salient Rotor with Modular Rotor Permanent Magnet of Flux Switching Machine

Soomro Irfan Ali,Sulaiman Erwan Bin,Ahmad Md Zarafi Bin,Aziz Roziah 대한전기학회 2023 Journal of Electrical Engineering & Technology Vol.18 No.4

Due to the large power/torque density and high efficiency of permanent magnet flux switching magnet (FSPM) are suitable for driving electric vehicles and hybrid electric vehicles. In this paper, flux switching permanent magnet machine (PMFSM), field excitation flux switching machine (FEFSM) and hybrid excitation flux switching machine (HEFSM) with salient rotor topology is compared with modular rotor permanent magnet flux switching machine. On the condition of the same outer diameters, the electromagnetic performances of the machines are analyzed and compared by the two-dimensional (2D) finite element method including the flux density, back electromotive force (EMF), cogging torque, electromagnetic torque, and power. The finite element results show that the modular rotor permanent magnet flux switching machine has greater torque than salient rotor FEFSM, HEFSM, and PMFSM at the rated load with significantly less weight than the others.

Sentiment Analysis of Product Reviews to Identify Deceptive Rating Information in Social Media: A SentiDeceptive Approach

M. Irfan Marwat,Javed Ali Khan,Dr. Mohammad Dahman Alshehri,Muhammad Asghar Ali,Hizbullah,Haider Ali,Muhammad Assam 한국인터넷정보학회 2022 KSII Transactions on Internet and Information Syst Vol.16 No.3

[Introduction] Nowadays, many companies are shifting their businesses online due to the growing trend among customers to buy and shop online, as people prefer online purchasing products. [Problem] Users share a vast amount of information about products, making it difficult and challenging for the end-users to make certain decisions. [Motivation] Therefore, we need a mechanism to automatically analyze end-user opinions, thoughts, or feelings in the social media platform about the products that might be useful for the customers to make or change their decisions about buying or purchasing specific products. [Proposed Solution] For this purpose, we proposed an automated SentiDecpective approach, which classifies end-user reviews into negative, positive, and neutral sentiments and identifies deceptive crowd-users rating information in the social media platform to help the user in decision-making. [Methodology] For this purpose, we first collected 11781 end-users comments from the Amazon store and Flipkart web application covering distant products, such as watches, mobile, shoes, clothes, and perfumes. Next, we develop a coding guideline used as a base for the comments annotation process. We then applied the content analysis approach and existing VADER library to annotate the end-user comments in the data set with the identified codes, which results in a labelled data set used as an input to the machine learning classifiers. Finally, we applied the sentiment analysis approach to identify the end-users opinions and overcome the deceptive rating information in the social media platforms by first preprocessing the input data to remove the irrelevant (stop words, special characters, etc.) data from the dataset, employing two standard resampling approaches to balance the data set, i-e, oversampling, and under-sampling, extract different features (TF-IDF and BOW) from the textual data in the data set and then train & test the machine learning algorithms by applying a standard cross-validation approach (KFold and Shuffle Split). [Results/Outcomes] Furthermore, to support our research study, we developed an automated tool that automatically analyzes each customer feedback and displays the collective sentiments of customers about a specific product with the help of a graph, which helps customers to make certain decisions. In a nutshell, our proposed sentiments approach produces good results when identifying the customer sentiments from the online user feedbacks, i-e, obtained an average 94.01% precision, 93.69% recall, and 93.81% F-measure value for classifying positive sentiments.

Spatial variations in COVID-19 risk perception and coping mechanism in Pakistan

Irfan Ahmad Rana,Saad Saleem Bhatti,Junaid Ahmad,Atif Bilal Aslam,Ali Jamshed 대한공간정보학회 2023 Spatial Information Research Vol.31 No.3

The outbreak of novel coronavirus disease (COVID-19) was declared a pandemic by the World Health Organization, which instigated governments to impose lockdowns across their countries. Amidst the lockdown in Pakistan, this study comprised measures of the COVID-19 risk perception, coping mechanism, and spatial variations. The data from 40 selected indicators was collected using an online questionnaire and grouped into domains (4 risk perception and 3 coping mechanisms domains). The results revealed the spatial variations and the levels of risk perception and coping mechanisms within the study area. Relative to each other, overall risk perception was highest in Northern Areas (Gilgit-Baltistan and Azad Jammu and Kashmir) and Islamabad, and lowest in Balochistan province. Very little spatial variation was observed in terms of coping mechanisms. Age, gender, and marital status influenced the risk perception associated with COVID-19. The findings suggest spatial variation in risk perception, implying the need for localized and modified COVID-19 risk communication and risk reduction strategies.