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Sentiment Analysis in Social Media for Competitive Environment Using Content Analysis

Computers, Materials & Continua 2022年第6期71卷 5603-5618页

作者： Shahid Mehmood imran Ahmad Muhammad Adnan khan Faheem khan T.Whangbo Riphah School of Computing&Innovation Faculty of ComputingRiphah International University Lahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Department of Computer Engineering Gachon UniversitySeongnam13557Korea

Education sector has witnessed several changes in the recent *** changes have forced private universities into fierce competition with each other to get more students *** competition has resulted in the adoption of marketing practices by private universities similar to commercial *** get competitive gain,universities must observe and examine the students’feedback on their own social media sites along with the social media sites of their *** study presents a novel framework which integrates numerous analytical approaches including statistical analysis,sentiment analysis,and text mining to accomplish a competitive analysis of social media sites of the *** techniques enable local universities to utilize social media for the identification of the most-discussed topics by students as well as based on the most unfavorable comments received,major areas for improvement.A comprehensive case study was conducted utilizing the proposed framework for competitive analysis of few top ranked international universities as well as local private universities in Lahore *** results show that diversity of shared content,frequency of posts,and schedule of updates,are the key areas for improvement for the local *** on the competitive intelligence gained several recommendations are included in this paper that would enable local universities generally and Riphah international university(RIU)Lahore specifically to promote their brand and increase their attractiveness for potential students using social media and launch successful marketing campaigns targeting a large number of audiences at significantly reduced cost resulting in an increased number of enrolments.

关键词： Social media higher education sentiment analysis content analysis competitive analysis text mining

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Perceptual quality assessment of panoramic stitched contents for immersive applications:a prospective survey

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Virtual Reality & Intelligent Hardware 2022年第3期4卷 223-246页

作者： Hayat ULLAH Sitara AFZAL imran Ullah khan Department of Computer Science Kansas State UniversityManhattanKS 66506USA Department of Software Sejong UniversitySeoul 143-747Republic of Korea

The recent advancements in the field of Virtual Reality(VR)and Augmented Reality(AR)have a substantial impact on modern day technology by digitizing each and everything related to human life and open the doors to the next generation Software Technology(Soft Tech).VR and AR technology provide astonishing immersive contents with the help of high quality stitched panoramic contents and 360°imagery that widely used in the education,gaming,entertainment,and production *** immersive quality of VR and AR contents are greatly dependent on the perceptual quality of panoramic or 360°images,in fact a minor visual distortion can significantly degrade the overall ***,to ensure the quality of constructed panoramic contents for VR and AR applications,numerous Stitched Image Quality Assessment(SIQA)methods have been proposed to assess the quality of panoramic contents before using in VR and *** this survey,we provide a detailed overview of the SIQA literature and exclusively focus on objective SIQA methods presented till *** better understanding,the objective SIQA methods are classified into two classes namely Full-Reference SIQA and No-Reference SIQA *** class is further categorized into traditional and deep learning-based methods and examined their performance for SIQA ***,we shortlist the publicly available benchmark SIQA datasets and evaluation metrices used for quality assessment of panoramic *** last,we highlight the current challenges in this area based on the existing SIQA methods and suggest future research directions that need to be target for further improvement in SIQA domain.

关键词： Virtual reality Augmented reality Panoramic image Immersive contents Stitched image quality assessment Deep learning Convolutional neural networks

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Image-Based Automatic Energy Meter Reading Using Deep Learning

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Computers, Materials & Continua 2023年第1期74卷 203-216页

作者： Muhammad imran Hafeez Anwar Muhammad Tufail Abdullah khan Murad khan Dzati Athiar Ramli Institute of Computer Sciences and Information Technology Faculty of Management and Computer SciencesThe University of AgriculturePeshawarPakistan Department of Electrical and Computer Engineering COMSATS University IslamabadAttock CampusAttock CityPakistan Kuwait College of Science and Technology Kuwait CityKuwait School of Electrical&Electronic Engineering USM Engineering CampusUniversiti Sains MalaysiaPenang14300Malaysia

We propose to perform an image-based framework for electrical energy meter *** aim is to extract the image region that depicts the digits and then recognize them to record the consumed *** the readings of serial numbers and energy meter units,an automatic billing system using the Internet of Things and a graphical user interface is deployable in a real-time ***,such region extraction and character recognition become challenging due to image variations caused by several factors such as partial occlusion due to dust on the meter display,orientation and scale variations caused by camera positioning,and non-uniform illumination caused by *** this end,our work evaluates and compares the stateof-the art deep learning algorithm You Only Look Once(YOLO)along with traditional handcrafted features for text extraction and *** image dataset contains 10,000 images of electrical energymeters and is further expanded by data augmentation such as in-plane rotation and scaling tomake the deep learning algorithms robust to these image *** training and evaluation,the image dataset is annotated to produce the ground truth of all the ***,YOLO achieves superior performance over the traditional handcrafted features with an average recognition rate of 98%for all the *** proves to be robust against the mentioned image variations compared with the traditional handcrafted *** proposed method can be highly instrumental in reducing the time and effort involved in the currentmeter reading,where workers visit door to door,take images ofmeters and manually extract readings from these images.

关键词： Convolutional neural network object localization machine learning

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A simple and facile iodination method of didechlorotiacumicin B and aromatic compounds

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Science China Chemistry 2021年第10期64卷 1736-1742页

作者： Haibo Zhang Liping Zhang imran khan Guangtao Zhang Yiguang Zhu Changsheng Zhang Key Laboratory of Tropical Marine Bio-resources and Ecology Guangdong Key Laboratory of Marine Materia MedicaRNAM Center for Marine MicrobiologySouth China Sea Institute of OceanologyChinese Academy of SciencesGuangzhou 510301China Sanya Institute of Oceanology SCSIOYazhou Scientific BaySanya 572000China Southern Marine Science and Engineering Guangdong Laboratory(Guangzhou) Guangzhou 511458China University of Chinese Academy of Sciences Beijing 100049China

Tiacumicin B(also known as fidaxomicin or difimicin)is a marketed 18-membered macrolide antibiotic for the treatment of Clostridium difficile *** B is structurally characterized with two chlorine atoms substituted on the aromatic ring,which is installed by the halogenase Tia *** the study on halide compatibility of Tia M,a facile and nonenzymatic iodination method of didechlorotiacumicin B was discovered,requiring Na I,acid and air for iodination at room *** method was also amenable to other 9 aromatic substrates with electron-rich sites for iodination.

关键词： iodination tiacumicin metal-free facile chemistry

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Integrated Machine Learning and Deep Learning Models for Cardiovascular Disease Risk Prediction: A Comprehensive Comparative Study

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Journal of Intelligent Learning Systems and Applications 2024年第1期16卷 12-22页

作者： Shadman mahmood khan Pathan Sakan Binte imran Departmentof Electrical and Computer Engineering Old Dominion University Norfolk USA Sir Salimullah Medical College Dhaka Bangladesh

Cardiovascular Diseases (CVDs) pose a significant global health challenge, necessitating accurate risk prediction for effective preventive measures. This comprehensive comparative study explores the performance of traditional Machine Learning (ML) and Deep Learning (DL) models in predicting CVD risk, utilizing a meticulously curated dataset derived from health records. Rigorous preprocessing, including normalization and outlier removal, enhances model robustness. Diverse ML models (Logistic Regression, Random Forest, Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Gradient Boosting) are compared with a Long Short-Term Memory (LSTM) neural network for DL. Evaluation metrics include accuracy, ROC AUC, computation time, and memory usage. Results identify the Gradient Boosting Classifier and LSTM as top performers, demonstrating high accuracy and ROC AUC scores. Comparative analyses highlight model strengths and limitations, contributing valuable insights for optimizing predictive strategies. This study advances predictive analytics for cardiovascular health, with implications for personalized medicine. The findings underscore the versatility of intelligent systems in addressing health challenges, emphasizing the broader applications of ML and DL in disease identification beyond cardiovascular health.

关键词： Cardiovascular Disease Machine Learning Deep Learning Predictive Modeling Risk Assessment Comparative Analysis Gradient Boosting LSTM

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Chemical composition, mechanism of antibacterial action and antioxidant activity of leaf essential oil of Forsythia koreana deciduous shrub

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Asian Pacific Journal of Tropical Medicine 2015年第9期8卷 682-688页

作者： Xiao-Nan Yang imran khan Sun Chul Kang Department of Biotechnology College of Engineering Daegu University Kyungsan

Objective: To identify the chemical constituents of leaf essential oil of Forsythia koreana(F. koreana) and evaluate its ef ects on bacterial strains. Methods: The essential oil of leaf of F. koreana was extracted by using hydrodistillation process and the volatile components investigated with the help of gas chromatography coupled with mass spectrometry. The antibacterial study was carried out with the help of agar disc dif usion method, MIC, MBC and viable count. The mode of action was determined with help of potassium ion l ux, cellular material release and scanning electron microscopy. The antioxidant activity was determined with the help of 2, 3-diphenyl-2-picrylhydrazyl method, nitric oxide scavenging activity and superoxide anion radical scavenging assay. Results: Total ten compounds were identii ed as trans-phytol(42.73%), cis-3-hexenol(12.95%), 毬-linalool(10.68%), trans-2-hexenal(8.86%), trans-2-hexenol(8.86%), myrcenol(3.86%), 4-vinylphenyl acetate(3.86%),(4Z)-4,6-heptadien-1-ol(3.18%), lemonol(2.73%) and benzeneacetaldehyde(2.27%) by gas chromatography coupled with mass spectrometry. The antibacterial study was demonstrated that leaf essential oil of F. koreana act against foodborne and other pathogenic bacteria. The mode of action revealed that this essential oil acted on the cytoplasmic membrane, resulting in loss of integrity and increased permeability. In addition, leaf essential oil of F. koreana was shown to be rich in linalool, which contributes to improved antioxidant activity. Conclusions: These results show that leaf essential oil of F. koreana has great potential as a natural food preservative, antibacterial and antioxidant agent.

关键词： Antibacterial activity Antioxidant activity Cell material released Essential oil GC-MS Potassium ion flux

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Gastrointestinal Diseases Classification Using Deep Transfer Learning and Features Optimization

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Computers, Materials & Continua 2023年第4期75卷 2227-2245页

作者： Mousa Alhajlah Muhammad Nouman Noor Muhammad Nazir Awais mahmood imran Ashraf Tehmina Karamat College of Applied Computer Science King Saud University(Almuzahmiyah Campus)Riyadh11543Saudi Arabia Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Computer Engineering HITEC UniversityTaxila47080Pakistan Department of Software Engineering Foundation University IslamabadIslamabad44000Pakistan

Gastrointestinal diseases like ulcers, polyps’, and bleeding areincreasing rapidly in the world over the last decade. On average 0.7 millioncases are reported worldwide every year. The main cause of gastrointestinaldiseases is a Helicobacter Pylori (H. Pylori) bacterium that presents in morethan 50% of people around the globe. Many researchers have proposeddifferent methods for gastrointestinal disease using computer vision *** of them focused on the detection process and the rest of themperformed classification. The major challenges that they faced are the similarityof infected and healthy regions that misleads the correct classificationaccuracy. In this work, we proposed a technique based on Mask Recurrent-Convolutional Neural Network (R-CNN) and fine-tuned pre-trainedResNet-50 and ResNet-152 networks for feature extraction. Initially, the region ofinterest is detected using Mask R-CNN which is later utilized for the trainingof fine-tuned models through transfer learning. Features are extracted fromfine-tuned models that are later fused using a serial approach. Moreover, anImproved Ant Colony Optimization (ACO) algorithm has also opted for thebest feature selection from the fused feature vector. The best-selected featuresare finally classified using machine learning techniques. The experimentalprocess was conducted on the publicly available dataset and obtained animproved accuracy of 96.43%. In comparison with state-of-the-art techniques,it is observed that the proposed accuracy is improved.

关键词： Diseases deep learning endoscopy gastrointestinal tract transfer learning

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Proportional Fairness-Based Power Allocation Algorithm for Downlink NOMA 5G Wireless Networks

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Computers, Materials & Continua 2020年第11期65卷 1571-1590页

作者： Jianzhong Li Dexiang Mei Dong Deng imran khan Peerapong Uthansakul School of Finance Chongqing Technology and Business UniversityChongqingChina Department of Electrical Engineering University of Engineering and TechnologyPeshawarPakistan School of Telecommunication Engineering Suranaree University of TechnologyNakhon RatchasimaThailand

Non-orthogonal multiple access(NOMA)is one of the key 5G technology which can improve spectrum efficiency and increase the number of user connections by utilizing the resources in a non-orthogonal *** allows multiple terminals to share the same resource unit at the same *** receiver usually needs to configure successive interference cancellation(SIC).The receiver eliminates co-channel interference(CCI)between users and it can significantly improve the system *** order to meet the demands of users and improve fairness among them,this paper proposes a new power allocation *** objective is to maximize user fairness by deploying the least fairness in multiplexed ***,the objective function obtained is non-convex which is converted into convex form by utilizing the optimal Karush-Kuhn-Tucker(KKT)*** results show that the proposed power allocation scheme gives better performance than the existing schemes which indicates the effectiveness of the proposed scheme.

关键词： 5G NOMA user fairness resource optimization multiple access scheme

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Leveraging Augmented Reality,Semantic-Segmentation,and VANETs for Enhanced Driver’s Safety Assistance

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Computers, Materials & Continua 2024年第1期78卷 1443-1460页

作者： Sitara Afzal imran Ullah khan Irfan Mehmood Jong Weon Lee Mixed Reality and Interaction Lab Department of SoftwareSejong UniversitySeoul05006Korea Faculty of Engineering and Digital Technologies School of Computer ScienceAI and ElectronicsUniversity of BradfordBradfordUK

Overtaking is a crucial maneuver in road transportation that requires a clear view of the road ***,limited visibility of ahead vehicles can often make it challenging for drivers to assess the safety of overtaking maneuvers,leading to accidents and *** this paper,we consider atrous convolution,a powerful tool for explicitly adjusting the field-of-view of a filter as well as controlling the resolution of feature responses generated by Deep Convolutional Neural Networks in the context of semantic image *** article explores the potential of seeing-through vehicles as a solution to enhance overtaking ***-through vehicles leverage advanced technologies such as cameras,sensors,and displays to provide drivers with a real-time view of the vehicle ahead,including the areas hidden from their direct line of *** address the problems of safe passing and occlusion by huge vehicles,we designed a see-through vehicle system in this study,we employed a windshield display in the back car together with cameras in both *** server within the back car was used to segment the car,and the segmented portion of the car displayed the video from the front *** see-through system improves the driver’s field of vision and helps him change lanes,cross a large car that is blocking their view,and safely overtake other *** network was trained and tested on the Cityscape dataset using semantic *** transparent technique will instruct the driver on the concealed traffic situation that the front vehicle has *** our findings,we have achieved 97.1% *** article also discusses the challenges and opportunities of implementing see-through vehicles in real-world scenarios,including technical,regulatory,and user acceptance factors.

关键词： Overtaking safety augmented reality VANET V2V deep learning

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Automated Patient Discomfort Detection Using Deep Learning

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Computers, Materials & Continua 2022年第5期71卷 2559-2577页

作者： imran Ahmed Iqbal khan Misbah Ahmad Awais Adnan Hanan Aljuaid Center of Excellence in Information Technology Institute of Management SciencesPeshawarPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman University(PNU)RiyadhSaudi Arabia

The Internet of Things(IoT)has been transformed almost all fields of life,but its impact on the healthcare sector has been *** IoTbased sensors are used in the healthcare sector and offer quality and safe care to *** work presents a deep learning-based automated patient discomfort detection system in which patients’discomfort is non-invasively *** do this,the overhead view patients’data set has been *** testing and evaluation purposes,we investigate the power of deep learning by choosing a Convolution Neural Network(CNN)based *** model uses confidence maps and detects 18 different key points at various locations of the body of the *** association rules and part affinity fields,the detected key points are later converted into six main body ***,the distance of subsequent key points is measured using coordinates ***,distance and the time-based threshold are used for the classification of movements associated with discomfort or normal *** accuracy of the proposed system is assessed on various test *** experimental outcomes reveal the worth of the proposed system’by obtaining a True Positive Rate of 98%with a 2%False Positive Rate.

关键词： Artificial intelligence patient monitoring discomfort detection deep learning

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