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| 1 | Empirical Analysis of Software Success Rate Forecasting During Requirement Engineering Processes显示文摘Forecasting on success or failure of software has become an interesting and,in fact,an essential task in the software development industry.In order to explore the latest data on successes and failures,this research focused on certain questions such as is early phase of the software development life cycle better than later phases in predicting software success and avoiding high rework?What human factors contribute to success or failure of a software?What software practices are used by the industry practitioners to achieve high quality of software in their day-to-day work?In order to conduct this empirical analysis a total of 104 practitioners were recruited to determine how human factors,misinterpretation,and miscommunication of requirements and decision-making processes play their roles in software success forecasting.We discussed a potential relationship between forecasting of software success or failure and the development processes.We noticed that experienced participants had more confidence in their practices and responded to the questionnaire in this empirical study,and they were more likely to rate software success forecasting linking to the development processes.Our analysis also shows that cognitive bias is the central human factor that negatively affects forecasting of software success rate.The results of this empirical study also validated that requirements’misinterpretation and miscommunication were themain causes behind software systems’failure.It has been seen that reliable,relevant,and trustworthy sources of information help in decision-making to predict software systems’success in the software industry.This empirical study highlights a need for other software practitioners to avoid such bias while working on software projects.Future investigation can be performed to identify the other human factors that may impact software systems’success. | Muhammad Hasnain Imran Ghani Seung Ryul Jeong Muhammad Fermi Pasha Sardar Usman Anjum Abbas | 2023 | Computers, Materials & Continua2023，，1： | 1 |
| 2 | Fake News Classification: Past, Current, and Future显示文摘The proliferation of deluding data such as fake news and phony audits on news web journals,online publications,and internet business apps has been aided by the availability of the web,cell phones,and social media.Individuals can quickly fabricate comments and news on social media.The most difficult challenge is determining which news is real or fake.Accordingly,tracking down programmed techniques to recognize fake news online is imperative.With an emphasis on false news,this study presents the evolution of artificial intelligence techniques for detecting spurious social media content.This study shows past,current,and possible methods that can be used in the future for fake news classification.Two different publicly available datasets containing political news are utilized for performing experiments.Sixteen supervised learning algorithms are used,and their results show that conventional Machine Learning(ML)algorithms that were used in the past perform better on shorter text classification.In contrast,the currently used Recurrent Neural Network(RNN)and transformer-based algorithms perform better on longer text.Additionally,a brief comparison of all these techniques is provided,and it concluded that transformers have the potential to revolutionize Natural Language Processing(NLP)methods in the near future. | Muhammad Usman Ghani Khan Abid Mehmood Mourad Elhadef Shehzad Ashraf Chaudhry | 2023 | Computers, Materials & Continua2023，77，11： | 0 |
| 3 | Mn-based MXene with high lithium-ion storage capacity显示文摘3d-transition metal(Fe,Co,Ni,and Mn)-based MXene materials have been predicted to demonstrate exceptional electrochemical performance because of their good electrical conductivity and the presence of metallic atoms with multiple charge states.However,until now,there have been no reports on MXenes based on Fe,Co,Ni,and Mn,due to the lack of 3d-metal-layered precursors.Herein,we successfully synthesized the first 3d-transition metal-based MXenes,Mn_(2)CT_(x) by exfoliating a layered precursor derived from the anti-perovskite bulk Mn3GaC.The as-prepared Mn_(2)CT_(x) MXene nanosheets were employed as anode materials in lithium-ion batteries,which exhibited stable storage capacity of 764.7 mAh·g^(-1) at 0.5 C,placing its storage capacities at an upper-middle level compared with other reported MXene materials as well as other Mn-based anode materials.Overall,this study opens a new avenue for MXene research by synthesizing 3d-transition metal-based MXenes for electrochemical applications. | Yanyan Wu Dongqing Liu Xiaonan Wang Usman Ghani Muhammad Asim Mushtaq Jinfeng Yang Huarui Sun Panagiotis Tsiakaras Xingke Cai | 2024 | Nano Research2024，17，5： | 0 |
| 4 | Antagonistic Potential of Bacterial Species against Fungal Plant Pathogens(FPP)and Their Role in Plant Growth Promotion(PGP):A Review显示文摘Since the 19th century to date,the fungal pathogens have been involved in causing devastating diseases in plants.All types of fungal pathogens have been observed in important agricultural crops that lead to significant pre and postharvest losses.The application of synthetic fungicide against the fungal plant pathogens(FPP)is a traditional management practice but at the same time these fungicides kill other beneficial microbes,insects,animal,and humans and are harmful to environment.The antagonistic microorganism such as bacteria are being used as an alternate strategy to control the FPP.These antagonistic species are cost-effective and eco-friendly in nature.These biocontrol bacteria have a broad mechanism against fungal pathogens present in the phyllosphere and rhizosphere of the plant.The antagonistic bacteria have different strategies against the FPP,by producing siderophore,biofilm,volatile organic compounds(VOCs),through parasitism,antibiosis,competition for limited resources and induce systemic resistance(ISR)in the host plant by activating the immune systems.The commercial bio-products synthesized by the major bacterial species Pseudomonas syringae,Burkholderia cepacia,Streptomyces griseoviridis,Pseudomonas fluorescens and Bacillus subtilis are used to control Fusarium,Pythium,Rhizoctonia,Penicillium,Alternaria,and Geotrichum.The commercial bio-formulations of bacteria act as both antifungal and plant growth regulators.The Plant growth-promoting rhizobacteria(PGPR)played a significant role in improving plant health by nitrogen-fixing,phosphorus solubilization,phytohormones production,minimizing soil metal contamination,and by ACC deaminase antifungal activities.Different articles are available on the specific antifungal activity of bacteria in plant diseases.Therefore,this review article has summarized the information on biocontrol activity of bacteria against the FPP and the role of PGPR in plant growth promotion.This review also provided a complete picture of scattered information regarding antifungal activities of bacteria and the role of PGPR. | Amjad Ali Yasir Iftikhar Mustansar Mubeen Haider Ali Muhammad Ahmad Zeshan Zohaib Asad Muhammad Zafar-ul-Hye Malik Abdul Rehman Mazhar Abbas Muhammad Rafique Muhammad Usman Ghani | 2022 | Phyton-International Journal of Experimental Botany2022，91，9： | 0 |
| 5 | Cognitive Skill Enhancement System Using Neuro-Feedback for ADHD Patients显示文摘The National Health Interview Survey(NHIS)shows that there are 13.2%of children at the age of 11 to 17 who are suffering from Attention Deficit Hyperactivity Disorder(ADHD),globally.The treatment methods for ADHD are either psycho-stimulant medications or cognitive therapy.These traditional methods,namely therapy,need a large number of visits to hospitals and include medication.Neurogames could be used for the effective treatment of ADHD.It could be a helpful tool in improving children and ADHD patients’cognitive skills by using Brain–Computer Interfaces(BCI).BCI enables the user to interact with the computer through brain activity using Electroencephalography(EEG),which can be used to control different computer applications by processing acquired brain signals.This paper proposes a system based on neurofeedback that can improve cognitive skills such as attention level,mediation level,and spatial memory.The proposed system consists of a puzzle game where its complexity increases with each level.EEG signals were acquired using the Neurosky headset;then sent the signals to the designed gaming environment.This neurofeedback system was tested on 10 different subjects,and their performance was calculated using different evaluation measures.The results show that this game improves player overall performance from 74%to 98%by playing each game level. | Muhammad Usman Ghani Khan Zubaira Naz Javeria Khan Tanzila Saba Ibrahim Abunadi Amjad Rehman Usman Tariq | 2021 | Computers, Materials & Continua2021，，8： | 0 |
| 6 | Acral melanoma detection using dermoscopic images and convolutional neural networks显示文摘Acral melanoma(AM)is a rare and lethal type of skin cancer.It can be diagnosed by expert dermatologists,using dermoscopic imaging.It is challenging for dermatologists to diagnose melanoma because of the very minor differences between melanoma and non-melanoma cancers.Most of the research on skin cancer diagnosis is related to the binary classification of lesions into melanoma and non-melanoma.However,to date,limited research has been conducted on the classification of melanoma subtypes.The current study investigated the effectiveness of dermoscopy and deep learning in classifying melanoma subtypes,such as,AM.In this study,we present a novel deep learning model,developed to classify skin cancer.We utilized a dermoscopic image dataset from the Yonsei University Health System South Korea for the classification of skin lesions.Various image processing and data augmentation techniques have been applied to develop a robust automated system for AM detection.Our custombuilt model is a seven-layered deep convolutional network that was trained from scratch.Additionally,transfer learning was utilized to compare the performance of our model,where AlexNet and ResNet-18 were modified,fine-tuned,and trained on the same dataset.We achieved improved results from our proposed model with an accuracy of more than 90%for AM and benign nevus,respectively.Additionally,using the transfer learning approach,we achieved an average accuracy of nearly 97%,which is comparable to that of state-of-the-art methods.From our analysis and results,we found that our model performed well and was able to effectively classify skin cancer.Our results show that the proposed system can be used by dermatologists in the clinical decision-making process for the early diagnosis of AM. | Qaiser Abbas Farheen Ramzan Muhammad Usman Ghani | 2021 | Visual Computing for Industry,Biomedicine,and Art2021，4，1： | 0 |
| 7 | Prediction of Extremist Behaviour and Suicide Bombing from Terrorism Contents Using Supervised Learning显示文摘This study proposes an architecture for the prediction of extremist human behaviour from projected suicide bombings.By linking‘dots’of police data comprising scattered information of people,groups,logistics,locations,communication,and spatiotemporal characters on different social media groups,the proposed architecture will spawn beneficial information.This useful information will,in turn,help the police both in predicting potential terrorist events and in investigating previous events.Furthermore,this architecture will aid in the identification of criminals and their associates and handlers.Terrorism is psychological warfare,which,in the broadest sense,can be defined as the utilisation of deliberate violence for economic,political or religious purposes.In this study,a supervised learning-based approach was adopted to develop the proposed architecture.The dataset was prepared from the suicide bomb blast data of Pakistan obtained from the South Asia Terrorism Portal(SATP).As the proposed architecture was simulated,the supervised learning-based classifiers na飗e Bayes and Hoeffding Tree reached 72.17%accuracy.One of the additional benefits this study offers is the ability to predict the target audience of potential suicide bomb blasts,which may be used to eliminate future threats or,at least,minimise the number of casualties and other property losses. | Nasir Mahmood Muhammad Usman Ghani Khan | 2022 | Computers, Materials & Continua2022，，3： | 0 |
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