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| 1 | Land-Cover Classification and its Impact on Peshawar’s Land Surface Temperature Using Remote Sensing显示文摘Spatial and temporal informationon urban infrastructure is essential and requires various land-cover/land-use planning and management applications.Besides,a change in infrastructure has a direct impact on other land-cover and climatic conditions.This study assessed changes in the rate and spatial distribution of Peshawar district’s infrastructure and its effects on Land Surface Temperature(LST)during the years 1996 and 2019.For this purpose,firstly,satellite images of bands7 and 8 ETM+(Enhanced Thematic Mapper)plus and OLI(Operational Land Imager)of 30 m resolution were taken.Secondly,for classification and image processing,remote sensing(RS)applications ENVI(Environment for Visualising Images)and GIS(Geographic Information System)were used.Thirdly,for better visualization and more in-depth analysis of land sat images,pre-processing techniques were employed.For Land use and Land cover(LU/LC)four types of land cover areas were identified-vegetation area,water cover,urbanized area,and infertile land for the years under research.The composition of red,green,and near infra-red bands was used for supervised classification.Classified images were extracted for analyzing the relative infrastructure change.A comparative analysis for the classification of images is performed for SVM(Support Vector Machine)and ANN(Artificial Neural Network).Based on analyzing these images,the result shows the rise in the average temperature from 30.04℃ to 45.25℃.This only possible reason is the increase in the built-up area from 78.73 to 332.78 Area km^(2) from 1996 to 2019.It has also been witnessed that the city’s sides are hotter than the city’s center due to the barren land on the borders. | Shahab Ul Islam Saifullah Jan Abdul Waheed Gulzar Mehmood Mahdi Zareei Faisal Alanazi | 2022 | Computers, Materials & Continua2022，，2： | 0 |
| 2 | Empirical Analysis of Neural Networks-Based Models for Phishing Website Classification Using Diverse Datasets显示文摘Phishing attacks pose a significant security threat by masquerading as trustworthy entities to steal sensitive information,a problem that persists despite user awareness.This study addresses the pressing issue of phishing attacks on websites and assesses the performance of three prominent Machine Learning(ML)models—Artificial Neural Networks(ANN),Convolutional Neural Networks(CNN),and Long Short-Term Memory(LSTM)—utilizing authentic datasets sourced from Kaggle and Mendeley repositories.Extensive experimentation and analysis reveal that the CNN model achieves a better accuracy of 98%.On the other hand,LSTM shows the lowest accuracy of 96%.These findings underscore the potential of ML techniques in enhancing phishing detection systems and bolstering cybersecurity measures against evolving phishing tactics,offering a promising avenue for safeguarding sensitive information and online security. | Shoaib Khan Bilal Khan Saifullah Jan Subhan Ullah Aiman | 2023 | Journal of Cyber Security2023，5，1： | 0 |
| 3 | Author’s Age and Gender Prediction on Hotel Review Using Machine Learning Techniques显示文摘Author’s Profile(AP)may only be displayed as an article,similar to text collection of material,and must differentiate between gender,age,education,occupation,local language,and relative personality traits.In several informationrelated fields,including security,forensics,and marketing,and medicine,AP prediction is a significant issue.For instance,it is important to comprehend who wrote the harassing communication.In essence,from a marketing perspective,businesses will get to know one another through examining items and websites on the internet.Accordingly,they will direct their efforts towards a certain gender or age restriction based on the kind of individuals who comment on their products.Recently many approaches have been presented many techniques to automatically detect user age and gender from the language which is based on text,documents,or comments on social media.The purpose of this research is to classify age(18–24,25–34,35–49,50–64,and 65–70)and gender(male,female)from a PAN 2014 Hotel Reviews dataset of the English language.The usage of six machine learning models is the main emphasis of this work,including the methods of Support Vector Machine(SVM),Random Forest(RF),Naive Bayes(NB),Logistic Regression(LR),Decision Tree(DT)and K-Nearest Neighbors(KNN). | Muhammad Hood Khan Bilal Khan Saifullah Jan Muhammad Imran Chughtai | 2023 | Journal on Big Data2023，5，1： | 0 |
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