Sign language is a non-verbal form of communication used by people with impaired hearing and speech. They also use facial actions to provide sign language prosody, similar to intonation in spoken languages. Sign Language Recognition (SLR) using hand signs is a typical way, however, face expression and body language play an important role in communication, which has not been analyzed to its fullest potential. In this paper, we present a dataset that comprises manual (hand signs) and non-manual (facial expressions and body movements) gestures of Pakistan Sign Language (PSL). It contains videos of 7 basic affective expressions performed by 100 healthy individuals, presented in an easily accessible format of .MP4 that can be used to train and test systems to make robust models for real-time applications using videos. Current data can also help with facial feature detection, classification of subjects by gender and age, or provide insights into any individual’s interest and emotional state.

This contains data and scripts used in the paper entitled "High resolution mapping of rural poverty in Pakistan with ensemble deep learning". The "README.me" file provides additional information about the scripts and underlying data.

External Debt: Public: Government: ML: Naya Pakistan Certificates data was reported at 1.055 USD bn in Dec 2024. This records an increase from the previous number of 888.798 USD mn for Sep 2024. External Debt: Public: Government: ML: Naya Pakistan Certificates data is updated quarterly, averaging 795.166 USD mn from Jun 2021 (Median) to Dec 2024, with 15 observations. The data reached an all-time high of 1.423 USD bn in Mar 2022 and a record low of 534.333 USD mn in Jun 2023. External Debt: Public: Government: ML: Naya Pakistan Certificates data remains active status in CEIC and is reported by State Bank of Pakistan. The data is categorized under Global Database’s Pakistan – Table PK.JB014: External Debt.

A number plate dataset is a collection of images and corresponding annotations that are used to train a machine learning model to recognize and locate number plates (also known as license plates) in images. The dataset typically consists of images of vehicles taken from various angles and under different lighting conditions, along with annotations specifying the location of the number plates in the images.

The goal of a number plate detection model is to accurately identify and locate the number plates in an image, regardless of the angle, lighting conditions, or background. This can be useful for applications such as automating the process of reading number plates for traffic monitoring, parking management, and vehicle identification.

The annotations in a number plate detection dataset may include the bounding box coordinates of the number plate in the image, as well as the text of the number plate. The dataset may also include additional metadata, such as the type of vehicle (car, truck, etc.), the location where the image was taken, and the date and time the image was taken.

Mobile Phones Prices in Pakistan, Dataset is a comprehensive collection of latest available mobile phones with detailed specifications. Information is extracted from the popular website, https://www.whatmobile.com/. This dataset comprises approximately 735 data points, each representing a unique mobile listing, and includes forty-two (42) distinct features.

License

CC0

Original Data Source: Mobile Phones Data Pakistan Sept-2023

This is a preprocessed dataset of 2 companies from Pakistan Stock Exchange.

Mali ML: Exports: fob: Emerging and Developing Economies: Middle East, North Africa, and Pakistan data was reported at 56.340 USD mn in 2017. This records a decrease from the previous number of 242.045 USD mn for 2016. Mali ML: Exports: fob: Emerging and Developing Economies: Middle East, North Africa, and Pakistan data is updated yearly, averaging 7.776 USD mn from Dec 1961 (Median) to 2017, with 56 observations. The data reached an all-time high of 242.045 USD mn in 2016 and a record low of 0.001 USD mn in 1979. Mali ML: Exports: fob: Emerging and Developing Economies: Middle East, North Africa, and Pakistan data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Mali – Table ML.IMF.DOT: Exports: fob: by Country: Annual.

Mali ML: Exports: fob: Emerging and Developing Economies: Middle East, North Africa, and Pakistan: Pakistan data was reported at 0.668 USD mn in 2017. This records an increase from the previous number of 0.397 USD mn for 2016. Mali ML: Exports: fob: Emerging and Developing Economies: Middle East, North Africa, and Pakistan: Pakistan data is updated yearly, averaging 0.475 USD mn from Dec 1984 (Median) to 2017, with 22 observations. The data reached an all-time high of 33.005 USD mn in 2004 and a record low of 0.000 USD mn in 1993. Mali ML: Exports: fob: Emerging and Developing Economies: Middle East, North Africa, and Pakistan: Pakistan data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Mali – Table ML.IMF.DOT: Exports: fob: by Country: Annual.

southwestern Pakistan) and its four regions of interest

Speech Emotion Recognition (SER) is a rapidly evolving field of research aimed at identifying and categorizing emotional states through the analysis of speech signals. As SER holds significant socio-cultural and commercial importance, researchers are increasingly leveraging machine learning and deep learning techniques to drive advancements in this domain. A high-quality dataset is an essential resource for SER studies in any language. Despite Urdu being the 10th most spoken language globally, there is a significant lack of robust SER datasets, creating a research gap. Existing Urdu SER datasets are often limited by their small size, narrow emotional range, and repetitive content, reducing their applicability in real-world scenarios. To address this gap, the Urdu Speech Emotion Corpus (UrSEC) was developed. This comprehensive dataset includes 3500 Urdu speech signals sourced from 10 professional actors, with an equal representation of male and female speakers from diverse age groups. The dataset encompasses seven emotional states: Angry, Fear, Boredom, Disgust, Happy, Neutral, and Sad. The speech samples were curated from a wide collection of Pakistani Urdu drama serials and telefilms available on YouTube, ensuring diversity and natural delivery. Unlike conventional datasets, which rely on predefined dialogs recorded in controlled environments, UrSEC features unique and contextually varied utterances, making it more realistic and applicable for practical applications. To ensure balance and consistency, the dataset contains 500 samples per emotional class, with 50 samples contributed by each actor for each emotion. Additionally, an accompanying Excel file provides detailed metadata for each recording, including the file name, duration, format, sample rate, actor details, emotional state, and corresponding Urdu dialog. This metadata enables researchers to efficiently organize and utilize the dataset for their specific needs. The UrSEC dataset underwent rigorous validation, integrating expert evaluation and model-based validation to ensure its reliability, accuracy, and overall suitability for advancing research and development in Urdu Speech Emotion Recognition.

Mali ML: Imports: cif: Emerging and Developing Economies: Middle East, North Africa, and Pakistan data was reported at 202.608 USD mn in 2017. This records a decrease from the previous number of 202.730 USD mn for 2016. Mali ML: Imports: cif: Emerging and Developing Economies: Middle East, North Africa, and Pakistan data is updated yearly, averaging 6.003 USD mn from Dec 1963 (Median) to 2017, with 55 observations. The data reached an all-time high of 202.730 USD mn in 2016 and a record low of 0.112 USD mn in 1977. Mali ML: Imports: cif: Emerging and Developing Economies: Middle East, North Africa, and Pakistan data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Mali – Table ML.IMF.DOT: Imports: cif: by Country: Annual.

Pakistan External Debt: Public: Government: ML: Military Debt data was reported at 0.000 USD mn in Jun 2018. This stayed constant from the previous number of 0.000 USD mn for Mar 2018. Pakistan External Debt: Public: Government: ML: Military Debt data is updated quarterly, averaging 71.250 USD mn from Jun 2006 (Median) to Jun 2018, with 49 observations. The data reached an all-time high of 199.000 USD mn in Dec 2009 and a record low of 0.000 USD mn in Jun 2018. Pakistan External Debt: Public: Government: ML: Military Debt data remains active status in CEIC and is reported by State Bank of Pakistan. The data is categorized under Global Database’s Pakistan – Table PK.JB011: External Debt.

Mapping land cover (LC) in mountainous regions, such as the Gilgit-Baltistan (GB) area of Pakistan, presents significant challenges due to complex terrain, limited data availability, and accessibility constraints. This study addresses these challenges by developing a robust, data-driven approach to classify LC using high-resolution Sentinel-2 (S-2) satellite imagery from 2019 within Google Earth Engine (GEE). The research evaluated the performance of various machine learning (ML) algorithms, including classification and regression tree (CART), maximum entropy (gmoMaxEnt), minimum distance (minDistance), support vector machine (SVM), and random forest (RF), without extensive hyperparameter tuning. Additionally, ten different scenarios based on various band combinations of S-2 data were used as input for running the ML models. The LC classification was performed using 2759 sample points, with 70% for training and 30% for validation. The results indicate that the RF algorithm outperformed all other classifiers under scenario S1 (using 10 bands), achieving an overall accuracy (OA) of 0.79 and a kappa coefficient of 0.76. The final RF-based LC mapping shows the following percentage distribution: barren land (46.7%), snow cover (22.9%), glacier (7.9%), grasses (7.2%), water (4.7%), wetland (2.9%), built-up (2.7%), agriculture (1.9%), and forest (1.2%). It is suggested that the best identified RF classifier within the GEE environment should be used for advanced multi-source data image classification with hyperparameter tuning to increase OA. Additionally, it is suggested to build the capacity of various stakeholders in GB for better monitoring of LC changes and resource management using geospatial big data.

This repository contains data and codes to our work on modeling XAI approach for Optimizing Ozone Delignification of Lignocellulose.Authors:Muhammad Rizwan a (Email: m21f0147ds001@fecid.paf-iast.edu.pk)Muhammad Ahmad Khan a (Email: m21f0161ai020@fecid.paf-iast.edu.pk)Khurram Shahzad Baig b, * (Email: khurram.shahzad@paf-iast.edu.pk)a. School of Computing Sciences, Pak-Austria Fachhochschule: Institute of Applied Science and Technology, Mang, Haripur 22621, Pakistanb. Department of Chemical and Energy Engineering, Pak-Austria Fachhochschule: Institute of Applied Science and Technology, Mang, Haripur 22621, Pakistan.Corresponding author:Dr. Khurram Shahzad BaigDepartment of Chemical and Energy Engineering, Pak-Austria Fachhochschule: Institute of Applied Science and Technology, Mang, Haripur 22621, Pakistan.Contact: +92-335-6119996Email: khurram.shahzad@paf-iast.edu.pkProject description:This study explores the use of machine learning to enhance ozonation-based lignin removal from lignocellulosic biomass, a key step in biofuel production. Lignin, which makes up about one-third of biomass, hinders reactions with cellulose and hemicellulose. Using Pycaret, 19 machine learning models were tested, with the Extra Trees Regressor providing the best predictions. SHAP analysis was applied to interpret the model results. The findings highlight the potential of machine learning to optimize the delignification process, opening avenues for more efficient and eco-friendly methods in biofuel and chemical production.

Pakistan External Debt: Public: Government: ML: Euro or Sukuk Global Bonds data was reported at 7.300 USD bn in Jun 2018. This stayed constant from the previous number of 7.300 USD bn for Mar 2018. Pakistan External Debt: Public: Government: ML: Euro or Sukuk Global Bonds data is updated quarterly, averaging 2.150 USD bn from Jun 2006 (Median) to Jun 2018, with 49 observations. The data reached an all-time high of 7.300 USD bn in Jun 2018 and a record low of 1.550 USD bn in Mar 2014. Pakistan External Debt: Public: Government: ML: Euro or Sukuk Global Bonds data remains active status in CEIC and is reported by State Bank of Pakistan. The data is categorized under Global Database’s Pakistan – Table PK.JB011: External Debt.

Inocybe bhurbanensis is described and illustrated as a new species from Himalayan Moist Temperate forests of Pakistan. It is characterized by fibrillose, conical to convex, umbonate, brown to dark brown pileus, non-pruinose, fibrillose stipe with whitish tomentum at the base and smooth basidiospores that are larger (9 × 5.2 µm) and thicker caulocystidia (up to 21 m) as compared to the sister species Inocybe demetris. Phylogenetic analyses of a nuclear rDNA region encompassing the internal transcribed spacers 1 and 2 along with 5.8S rDNA (ITS) and the 28S rDNA D1–D2 domains (28S) also confirmed its novelty. Methods Collection and morphological characterization Collections were made on routine mycological visits to the Himalayan Forests of Pakistan during 2020–2022. Basidiomata were collected following Lodge et al. (2004) and photographed in their natural habitats. Descriptions of macromorphological characters were made based on fresh collections and color photographs. Colors were designated with reference to Munsell Soil Color Charts (1975). Microscopic characters were described based on free-hand sections from fresh and dried specimens mounted in 5% (w/v) aqueous Potassium Hydroxide (KOH) solution. Measurements of anatomical structures were taken with the calibrated computer-based software “PIXIMÈTRE version 5.9” connected to a compound microscope (BOECO, Model: BM120) and visualized through a microscopic camera (MVV 3000). A total of sixty basidiospores, basidia, different types of cystidia and hyphae were measured from all the collections. For measurements; Q is the range of length/width (L/W) ratio of the total measured basidiospores while av.Q is the average L × average W of all the measured basidiospores. DNA extraction DNA from herbarium specimens was extracted following the procedure of (Peintner et al. 2001). The primer pair ITS1F (Gardes and Bruns 1993) and ITS4 (White et al. 1990) were used to amplify the ITS region and the primer pair LR5 and LR0R (Vilgaly’s lab http://sites.biology.duke.edu/fungi/mycolab/primers.htm) was used to amplify the 28S region. Polymerase chain reactions (PCR) were performed in 25 μL volume per reaction. PCR procedure for the ITS region consisted of an initial 4 minutes denaturation at 94°C, 40 cycles of 1 minute at 94°C, 1 min at 55°C, 1 min at 72°C, and a final extension of 10 minutes at 72°C. The PCR procedure for the 28S region consisted of initial denaturation at 94°C for 2 minutes, 35 cycles of 94°C for 1 minute, 52°C for 1 minute, 72°C for 1 minute, and final extension at 72°C for 7 minutes. Visualization of PCR products was accomplished using 1% agarose gel added with 3 μL ethidium bromide and a UV illuminator. Sequencing of the amplified products was accomplished through outsourcing (BGI, Beijing Genomic Institute, Hong Kong). Phylogenetic analyses The ITS region of the voucher collections GB19, BR19 and AN-87 yielded 730, 752 and 780 bp fragments, respectively. Sequences of all three specimens were used as a reference to BLAST against GenBank. All the query sequences matched 91% with Inocybe demetris and 87% with Inocybe comis. Other closely related sequences were downloaded for high similarity with query sequences and used in the subsequent phylogenetic analyses. The 28S region yielded a 988 bp fragment for GB-19 and ANK-87. The query sequences (BLAST) showed 99% similarity and 94% query cover with I. demetris. DNA sequences were aligned using the online webPRANK tool at http://www.ebi.ac.uk/goldman-srv/webprank/ (Löytynoja and Goldman 2010). Maximum likelihood analyses for individual gene regions were performed via CIPRES Science Gateway (Miller et al. 2010) employing RAxML-HPC v.8. Rapid bootstrap analysis/search for best-scoring ML tree was configured for each dataset. For the bootstrapping phase, the GTRCAT model was selected. One thousand rapid bootstrap replicates were run. A bootstrap proportion of ≥ 70% was considered significant.

Pakistan External Debt: Scheduled Banks: Borrowing: ML: Private data was reported at 1.300 USD bn in Dec 2024. This stayed constant from the previous number of 1.300 USD bn for Sep 2024. Pakistan External Debt: Scheduled Banks: Borrowing: ML: Private data is updated quarterly, averaging 25.547 USD mn from Mar 2010 (Median) to Dec 2024, with 60 observations. The data reached an all-time high of 1.300 USD bn in Dec 2024 and a record low of 0.000 USD mn in Dec 2022. Pakistan External Debt: Scheduled Banks: Borrowing: ML: Private data remains active status in CEIC and is reported by State Bank of Pakistan. The data is categorized under Global Database’s Pakistan – Table PK.JB014: External Debt.

Mali ML: Trade Balance: Emerging and Developing Economies: Middle East, North Africa, and Pakistan: Pakistan data was reported at -1.761 USD mn in May 2018. This records a decrease from the previous number of -1.760 USD mn for Apr 2018. Mali ML: Trade Balance: Emerging and Developing Economies: Middle East, North Africa, and Pakistan: Pakistan data is updated monthly, averaging -0.025 USD mn from Dec 1963 (Median) to May 2018, with 268 observations. The data reached an all-time high of 7.566 USD mn in Apr 2004 and a record low of -3.325 USD mn in Jan 1986. Mali ML: Trade Balance: Emerging and Developing Economies: Middle East, North Africa, and Pakistan: Pakistan data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Mali – Table ML.IMF.DOT: Trade Balance: by Country: Monthly.

Pakistan External Debt: Public: Government: ML: Saudi Fund for Development data was reported at 20.000 USD mn in Jun 2018. This stayed constant from the previous number of 20.000 USD mn for Mar 2018. Pakistan External Debt: Public: Government: ML: Saudi Fund for Development data is updated quarterly, averaging 100.000 USD mn from Jun 2006 (Median) to Jun 2018, with 46 observations. The data reached an all-time high of 200.000 USD mn in Mar 2013 and a record low of 0.000 USD mn in Sep 2009. Pakistan External Debt: Public: Government: ML: Saudi Fund for Development data remains active status in CEIC and is reported by State Bank of Pakistan. The data is categorized under Global Database’s Pakistan – Table PK.JB011: External Debt.