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| 1 | Social Media and Stock Market Prediction: A Big Data Approach显示文摘Big data is the collection of large datasets from traditional and digital sources to identify trends and patterns.The quantity and variety of computer data are growing exponentially for many reasons.For example,retailers are building vast databases of customer sales activity.Organizations are working on logistics financial services,and public social media are sharing a vast quantity of sentiments related to sales price and products.Challenges of big data include volume and variety in both structured and unstructured data.In this paper,we implemented several machine learning models through Spark MLlib using PySpark,which is scalable,fast,easily integrated with other tools,and has better performance than the traditional models.We studied the stocks of 10 top companies,whose data include historical stock prices,with MLlib models such as linear regression,generalized linear regression,random forest,and decision tree.We implemented naive Bayes and logistic regression classification models.Experimental results suggest that linear regression,random forest,and generalized linear regression provide an accuracy of 80%-98%.The experimental results of the decision tree did not well predict share price movements in the stock market. | Mazhar Javed Awan Mohd Shafry Mohd Rahim Haitham Nobanee Ashna Munawar Awais Yasin Azlan Mohd Zain Azlanmz | 2021 | Computers, Materials & Continua2021，，5： | 0 |
| 2 | Artificial Intelligence Based Sentence Level Sentiment Analysis of COVID-19显示文摘Web-blogging sites such as Twitter and Facebook are heavily influenced by emotions,sentiments,and data in the modern era.Twitter,a widely used microblogging site where individuals share their thoughts in the form of tweets,has become a major source for sentiment analysis.In recent years,there has been a significant increase in demand for sentiment analysis to identify and classify opinions or expressions in text or tweets.Opinions or expressions of people about a particular topic,situation,person,or product can be identified from sentences and divided into three categories:positive for good,negative for bad,and neutral for mixed or confusing opinions.The process of analyzing changes in sentiment and the combination of these categories is known as“sentiment analysis.”In this study,sentiment analysis was performed on a dataset of 90,000 tweets using both deep learning and machine learning methods.The deep learning-based model long-short-term memory(LSTM)performed better than machine learning approaches.Long short-term memory achieved 87%accuracy,and the support vector machine(SVM)classifier achieved slightly worse results than LSTM at 86%.The study also tested binary classes of positive and negative,where LSTM and SVM both achieved 90%accuracy. | Sundas Rukhsar Mazhar Javed Awan Usman Naseem Dilovan Asaad Zebari Mazin Abed Mohammed Marwan Ali Albahar Mohammed Thanoon Amena Mahmoud | 2023 | Computer Systems Science & Engineering2023，47，10： | 0 |
| 3 | Osteoporosis Prediction for Trabecular Bone using Machine Learning: A Review显示文摘Trabecular bone holds the utmost importance due to its significance regarding early bone loss.Diseases like osteoporosis greatly affect the structure of the Trabecular bone which results in different outcomes like high risk of fracture.The objective of this paper is to inspect the characteristics of the Trabecular Bone by using the Magnetic Resonance Imaging(MRI)technique.These characteristics prove to be quite helpful in studying different studies related to Trabecular bone such as osteoporosis.The things that were considered before the selection of the articles for the systematic review were language,research field,and electronic sources.Only those articles written in the English language were selected as it is the most prominent language used in scientific,engineering,computer science,and biomedical researches.This literature review was conducted on the articles published between 2006 and 2020.A total of 62 research papers out of 1050 papers were extracted which were according to our topic of review after screening abstract and article content for the title and abstract screening.The findings from those researches were compiled at the end of the result section.This systematic literature review presents a comprehensive report on scientific researches and studies that have been done in the medical area concerning trabecular bone. | Marrium Anam Vasaki a/p Ponnusamy Muzammil Hussain Muhammad Waqas Nadeem Mazhar Javed Hock Guan Goh Sadia Qadeer | 2021 | Computers, Materials & Continua2021，，4： | 0 |
| 4 | AI-Enabled COVID-19 Outbreak Analysis and Prediction: Indian States vs. Union Territories显示文摘The COVID-19 disease has already spread to more than 213 countries and territories with infected(confirmed)cases of more than 27 million people throughout the world so far,while the numbers keep increasing.In India,this deadly disease was first detected on January 30,2020,in a student of Kerala who returned from Wuhan.Because of India’s high population density,different cultures,and diversity,it is a good idea to have a separate analysis of each state.Hence,this paper focuses on the comprehensive analysis of the effect of COVID-19 on Indian states and Union Territories and the development of a regression model to predict the number of discharge patients and deaths in each state.The performance of the proposed prediction framework is determined by using three machine learning regression algorithms,namely Polynomial Regression(PR),Decision Tree Regression,and Random Forest(RF)Regression.The results show a comparative analysis of the states and union territories having more than 1000 cases,and the trained model is validated by testing it on further dates.The performance is evaluated using the RMSE metrics.The results show that the Polynomial Regression with an RMSE value of 0.08,shows the best performance in the prediction of the discharged patients.In contrast,in the case of prediction of deaths,Random Forest with a value of 0.14,shows a better performance than other techniques. | Meenu Gupta Rachna Jain Simrann Arora Akash Gupta Mazhar Javed Awan Gopal Chaudhary Haitham Nobanee | 2021 | Computers, Materials & Continua2021，，4： | 0 |
| 5 | Identification of Software Bugs by Analyzing Natural Language-Based Requirements Using Optimized Deep Learning Features显示文摘Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty requirements.Researchers are exploring machine learning to predict software bugs,but a more precise and general approach is needed.Accurate bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning methods.However,these studies are not generalized and efficient when extended to other datasets.Therefore,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification problems.The methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a model.Four National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were combined.It reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode. | Qazi Mazhar ul Haq Fahim Arif Khursheed Aurangzeb Noor ul Ain Javed Ali Khan Saddaf Rubab Muhammad Shahid Anwar | 2024 | Computers, Materials & Continua2024，78，3： | 0 |
| 6 | Termites Improve the Horizontal Movement of Carbonized Particles:A Step towards Sustainable Utilization of Biochar显示文摘Soil amendments containing carbonized materials increase the soil carbon reservoir,influence plant productivity,and,ultimately,help to clean the environment.There is data on the effect of such additions on soil physicochemical properties or plant growth,but few studies have focused on how these carbonized materials are distributed by termite species in the soil ecosystem.It is the first comprehensive study of the transportation of biochar(BC)by termite species under tropical environmental conditions in Pakistan.The present study was carried out to test the hypothesis that if termite species I)were involved in the distribution of biochar particles II)if yes,then how far these particles were transported during the study period(10 days)and III)check their preference between the enriched BC(EBC)and non-enriched BC.BC was enriched with the cattle slurry after its pyrolysis in the study.The results showed that EBC particles were significantly more widely distributed than non-enriched BC particles,but both types of BC were transported more than 4 cm(ring 4)within 10 days(at the end of the experiment).The current study also revealed that EBC was easily attached to the setae,cuticle,and legs of termites,implying that it could potentially be transported over a greater distance.Furthermore,transportation of EBC over larger distances indicated a potential preference of termite species between the EBC and BC particles.During the study,however,the preference among the termite species was also observed.Under the prevailing study conditions,the Coptotermes heimi and Heteroterme indicola species transported the EBC further than Microtermes obesi and Odontotermes obesus.These findings revealed that transportation preferences were observed among the four termite species.In conclusion,the current study found that termites were involved in the distribution of BC particles,with a preference for EBC and that these have the potential to transport BC particles more than 4 cm within 10 days.Furthermore,two species Coptotermes heimi and Heteroterme indicola may be more suitable candidates for EBC transpiration in Pakistani soils.It was necessary to conduct additional research into the effect of temperature on the transportation process. | Mazhar Ali Nasir Masood Hafiz Muhammad Rashad Javeed Ibrahim Al-Ashkar Khalid F.Almutairi Liyun Liu Muhammad Aqeel Sarwar Karthika Rajendran Ayman EL Sabagh | 2022 | Phyton-International Journal of Experimental Botany2022，91，10： | 0 |
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