This study explores the impact of affective AI literacy on student satisfaction in Pakistan’s evolving higher education sector, which is placing greater emphasis on sustainable education and market-relevant skills.

In our research article titled, "Knowledge, Attitudes, and Perceptions of Healthcare Students and Professionals on the Use of Artificial Intelligence in Healthcare," we set out to explore the scope of AI understanding within the healthcare community in Pakistan. We were particularly motivated to bridge the existing gaps in knowledge and explore the once uncharted territories of AI perception among all healthcare staff, including nurses, medical students, and allied healthcare workers.

About Data set

This dataset contains detailed records of crimes reported, categorized by type. The data is collected from multiple sources to provide insights into crime trends over a specific time period. It is useful for law enforcement, policymakers, researchers, and data analysts interested in crime pattern analysis and public safety. Content: The dataset includes various types of reported crimes along with attributes such as the location, time period, and other relevant details. Each row represents a record of a specific crime report. ## Data Source: The data has been compiled from publicly available government records and law enforcement reports.

Usage:

This dataset can be used for:

Exploratory Data Analysis (EDA) to identify trends in crime rates by type. Building machine learning models to predict areas or types of crimes based on historical data. Visualizing crime data using plots or heatmaps to identify high-crime areas. Inspiration: Analyze crime trends over time. Identify which types of crimes are more prevalent in certain areas. Predict potential future crimes based on past data patterns.

The advent of artificial intelligence (AI) technologies has emerged as a promising solution to enhance healthcare efficiency and improve patient outcomes. The objective of this study is to analyse the knowledge, attitudes, and perceptions of healthcare professionals in Pakistan about AI in healthcare. We conducted a cross-sectional study using a questionnaire distributed via Google Forms. This was distributed to healthcare professionals (e.g., doctors, nurses, medical students, and allied healthcare workers) working or studying in Pakistan. Consent was taken from all participants before initiating the questionnaire. The questions were related to participant demographics, basic understanding of AI, AI in education and practice, AI applications in healthcare systems, AI’s impact on healthcare professions and the socio-ethical consequences of the use of AI. We analyzed the data using Statistical Package for Social Sciences (SPSS) statistical software, version 26.0. Overall, 616 individuals responded to the survey while n = 610 (99.0%) of respondents consented to participate. The mean age of participants was 32.2 ± 12.5 years. Most of the participants (78.7%, n = 480) had never received any formal sessions or training in AI during their studies/employment. A majority of participants, 70.3% (n = 429), believed that AI would raise more ethical challenges in healthcare. In all, 66.4% (n = 405) of participants believed that AI should be taught at the undergraduate level. The survey suggests that there is insufficient training about AI in healthcare in Pakistan despite the interest of many in this area. Future work in developing a tailored curriculum regarding AI in healthcare will help bridge the gap between the interest in use of AI and training.

👨‍💻 About the Author

This dataset is created by Muhammad Danyal Javed - 📧 Email: dani.ai.practitioner@gmail.com
- 🔗 LinkedIn | Google Scholar | ORCID

📖 Dataset Description

This dataset provides synthetic daily malaria case counts across malaria-prone districts in Pakistan from March 2020 to July 2025.

The data is generated through a simulation framework that integrates:

🌦️ Climate drivers (rainfall, temperature, humidity). 🦟 Transmission seasonality (monsoon-driven malaria peaks). 🛡️ Interventions (insecticide-treated nets, indoor residual spraying). 👥 Population scaling at district level.

⚠️ Important Note: This is synthetic (simulated) data created for research, learning, and prototyping. It does not contain real patient records.

📂 Files

features.csv (3.67 MB) Rows: 41,500 Columns: 9 Duplicates: None Missing Values: Intentionally included for teaching purposes (e.g., imputation techniques).

🔬 Data Generation Methodology

The dataset was generated using the following simulation process:

Seasonality: Transmission patterns modeled via sinusoidal curves aligned with Pakistan’s malaria seasons. Climate effects: Daily rainfall, temperature, and humidity simulated with Gaussian noise around observed regional averages.

Interventions: ITNs (insecticide-treated nets) → reduced simulated transmission probability. IRS (indoor residual spraying) → reduced case amplification in peak seasons. Population scaling: District-level populations used as weights for case intensity.

import pandas as pd import matplotlib.pyplot as plt

# Load dataset df = pd.read_csv("features.csv")

# Quick look print(df.head())

# Plot malaria cases vs rainfall (national average) df.groupby("date")[["cases", "rain"]].mean().plot(figsize=(12,5)) plt.title("Simulated Malaria Cases vs Rainfall (Pakistan, 2020–25)") plt.show()

The Presidential Initiative for Artificial Intelligence and Computing(PIAIC) was launched by the President of Pakistan, Dr. Arif Alvi, to promote education, research and business opportunities in Artificial Intelligence, Blockchain, Internet of Things, and Cloud Native Computing. The initiative comes in a bid to enable Pakistan in making an imprint on the world’s path towards the Fourth Industrial Revolution. It aims to transform the fields of education, research, and business in Pakistan. President Dr. Arif Alvi had launched PIAIC to reshape Pakistan by revolutionizing education, research and businesses through introducing latest cutting-edge technologies.

Data collected from https://results.piaic.org/ which consist of students enrolled in Artificial Intelligence, Blockchain, Internet of Things, and Cloud Native Computing. Each of these courses are 1 year and comprises of four quarters. Students performance is evaluated in 20% percentile in each quarter.

With the proliferation of AI tools, it is crucial to understand the level of AI literacy among university students. This study investigates the AI literacy of Library and Information Science (LIS) students in South Asia e.g., Bangladesh, India and Pakistan, and the Middle East namely Jordan, Lebanon and Saudi Arabia. A quantitative design was employed in this study, which targets 816 LIS students at various academic institutions.

Background and objectivesThe Extended Reality (XR) technology was established by combining elements of Virtual Reality and Augmented Reality, offering users the advantage of working in a virtual environment. The study aimed to evaluate medical professionals’ and students’ knowledge, attitudes, and practices regarding using XR technology in Pakistan’s healthcare system and identify its benefits, drawbacks, and implications for the system’s future.MethodologyA cross-sectional study was executed by circulating a self-structured online questionnaire among the Medical Community across Major Cities of Pakistan using various social media platforms as available sampling. The sample size was calculated to be 385 using RAOSOFT. Cronbach’s alpha was calculated as 0.74. The Exploratory Factor Analysis (EFA) conducted on the dataset was validated using the Kaiser-Meyer-Olkin (KMO) measure and Bartlett’s Test of Sphericity. The KMO value of 0.752 indicates adequate sampling, and Bartlett’s Test was significant (χ2 (435) = 2809.772, p < 0.001), confirming the suitability of the data for factor analysis. Statistical analysis was done using SPSS-25, and data description was done as frequency and percentage. Pearson correlation and regression analysis kept p-value < 0.05% significant.ResultsApproximately 54.8% of 406 participants conveyed their familiarity with XR technologies. The majority of participants (83.8%) believed that using XR technology effectively enhanced medical education and patient care in Pakistan. Regarding clinical outcomes, 70.8% believed XR improved the efficiency of procedures and 52.8% agreed XR would lead to more device-dependent systems and eradicating human error (32.4%). Major barriers to XR integration included ethical and privacy issues (63.9%), lack of technological advancements in Pakistan (70%), and lack of ample knowledge and training of XR among health care professionals (45.8%). Hypothesis testing revealed a low positive but significant correlation between the use of AI-based healthcare systems and the increasing speed and accuracy of procedures (r = 0.342, p < 0.001), supporting Hypothesis 1. Similarly, a very low positive yet significant correlation was observed between the augmentation of diagnostic and surgical procedures and addressing data security and ethical issues for implementing XR (r = 0.298, p < 0.001), supporting Hypothesis 2. Lastly, a correlation between the mean Attitude (MA) score and the mean Perception (MP) score was found to be moderately positive and significant (r = 0.356, p < 0.001). Hence, the hypothesis 3 was supported.ConclusionXR technology has the potential to enhance medical education and patient care in Pakistan, but its adoption faces significant challenges, including ethical concerns, technological gaps, and inadequate training. The study’s findings highlight the need to address these issues to maximize the benefits of XR in healthcare.

Context

Pakistan has a large number of public and private universities offering degrees in multiple disciplines. There are 162 universities out of which 64 are in private sector and 98 are public sector/government universities recognized by the Higher Education Commission of Pakistan (HEC).

According to HEC, Pakistani universities are producing over half a million graduates per year, which include over more than 10,000 Computer Science/IT graduates.

From year 2001 to 2015 there is a mass increase in number of enrollment in universities. The recent statistics shows that in 2015, 1,298,600 students enrolled in different levels of degree, 869,378 in Bachelors (16 years), 63,412 in Bachelors (17 years), 219,280 in Masters (16 years), 124,107 in M.Phil/MS, 14,373 in Ph.D, and 8,319 in P.G.D. However, in 2014 the number of doctoral degree awarded were 1,351 only.

Moreover, according to HEC report, in 2014-2015 there are over 10,125 fulltime Ph.D. faculty teaching in Pakistan in all disciplines. Computer Science and related disciplines are widely taught in Pakistan with over 90 universities offering this discipline with qualified faculty. According to our dataset, there are 504 PhD faculty members in Computer Science in Pakistan for 10,000 students. So we have a PhD faculty member for every 20 students on average in computer science program.

Current Student to PhD Professor Ratio in Pakistan is 130:1 (while India is going towards 10:1 in Post-Graduate and 25:1 in Undergrad education).

Here is world's Top 100 universities with Student to Staff Ratio.

Content

Dataset: The dataset contains list of computer science/IT professors from 89 different universities of Pakistan.

Variables: The dataset contains Serial No, Teacher’s Name, University Currently Teaching, Department, Province University Located, Designation, Terminal Degree, Graduated from (university for professor), Country of graduation, Year, Area of Specialization/Research Interests, and some Other Information

Acknowledgements

Data has been collected from respective university websites. Some of the universities did not mention about their faculty profiles or were unavailable (hence the limitation of this dataset). The statistics mentioned above are gathered by Higher Education Commission of Pakistan (HEC) website and other web resources.

Inspiration

Here is what I like you to do:

1. Which area of interest/expertise is in abundance in Pakistan and where we need more people?
2. How many professors we have in Data Sciences, Artificial Intelligence, or Machine Learning?
3. Which country and university hosted majority of our teachers?
4. Which research areas were most common in Pakistan?
5. How does Pakistan Student to PhD Professor Ratio compare against rest of the world, especially with USA, India and China?
6. Any visualization and patterns you can generate from this data

Let me know how I can improve this dataset and best of luck with your work

This dataset contains images of eight varieties of Pakistani mangoes. An experiment is performed on the proposed dataset for automated classification and grading of harvested mangoes to facilitate farmers in delivering high-quality mangoes on time for export, and a high accuracy is achieved using Convolutional Neural Network. Researchers and students can use this dataset to develop, test and evaluate different computer vision algorithms to contribute the improving agriculture sector. The provided dataset can be consider as a benchmark for testing and comparing the performance of different state-of-the-arts. We would like to thank the Haji Ghulam Muhammad Mangana mango farm (Registered) Multan Pakistan for database collection and (MRI) Mango Research Institute, Multan for guidance about the standardized grading of mango.

ABSTRACT The present study aimed at comparing predictive performance of some data mining algorithms (CART, CHAID, Exhaustive CHAID, MARS, MLP, and RBF) in biometrical data of Mengali rams. To compare the predictive capability of the algorithms, the biometrical data regarding body (body length, withers height, and heart girth) and testicular (testicular length, scrotal length, and scrotal circumference) measurements of Mengali rams in predicting live body weight were evaluated by most goodness of fit criteria. In addition, age was considered as a continuous independent variable. In this context, MARS data mining algorithm was used for the first time to predict body weight in two forms, without (MARS_1) and with interaction (MARS_2) terms. The superiority order in the predictive accuracy of the algorithms was found as CART > CHAID ≈ Exhaustive CHAID > MARS_2 > MARS_1 > RBF > MLP. Moreover, all tested algorithms provided a strong predictive accuracy for estimating body weight. However, MARS is the only algorithm that generated a prediction equation for body weight. Therefore, it is hoped that the available results might present a valuable contribution in terms of predicting body weight and describing the relationship between the body weight and body and testicular measurements in revealing breed standards and the conservation of indigenous gene sources for Mengali sheep breeding. Therefore, it will be possible to perform more profitable and productive sheep production. Use of data mining algorithms is useful for revealing the relationship between body weight and testicular traits in describing breed standards of Mengali sheep.

This dataset contains hourly air quality and meteorological data collected from August 2021 to December 2024 for five major Pakistani cities: Islamabad, Lahore, Karachi, Quetta, and Peshawar.

The data is organized into two main folders: - training/: Contains concatenated hourly data from August 2021 to July 2024 for all cities. - testing/: Contains separate files for each city with data from July 2024 to December 2024.

⚙️ Features Included:

• datetime
• main_aqi
• components_co, components_no, components_no2, components_o3, components_so2, components_pm2_5, components_pm10, components_nh3
• temperature_2m, relative_humidity_2m, dew_point_2m, precipitation, surface_pressure, wind_speed_10m, wind_direction_10m, shortwave_radiation

🌐 Data Sources:

• Air pollutant data is collected via the OpenWeatherMap API.
• Meteorological parameters (e.g., temperature, humidity, pressure) are collected using the Open-Meteo API.
• All data is fetched on an hourly interval using latitude/longitude coordinates specific to each city.

These APIs are open-source and free for research use. This dataset is suitable for time series analysis, air pollution forecasting, and environmental monitoring projects.

The global rise of Artificial Intelligence (AI) in English as a Second Language (ESL) education has shown promise, yet its application in resource-constrained contexts like Pakistan remains underexplored. This study examines the integration of AI tools in Pakistani ESL classrooms, with a focus on (1) teachers’ instructional practices, (2) student learning outcomes, and (3) implementation challenges. Using a mixed-methods approach, data were collected through classroom observations, focus group discussions, and pre- and post-tests on vocabulary and writing skills administered to 100 undergraduate students (50 in the experimental group and 50 in the control group) over 16 weeks. The experimental group, taught with AI tools such as Grammarly and QuillBot, demonstrated significantly greater gains in vocabulary (+45%, d = 1.12) and writing performance (+46%, d = 1.03) compared to the control group. Qualitative findings revealed that while AI tools supported grammar correction and vocabulary enhancement, their effectiveness was limited by infrastructural constraints, insufficient teacher training, and cultural misalignment in language feedback. The study concludes that AI can meaningfully enhance ESL instruction when paired with teacher facilitation and localized design. It offers novel insights into culturally responsive AI integration in under-resourced educational contexts.

Perception of pharmacy students towards artificial intelligence (n = 570).

X-rays of wrist fracture collected from Al-huda Digital X-ray Laboratory, Nishtar Road, Multan, Pakistan.

Snapshot img

Automation In Textile Industry Market Size 2025-2029

The automation in textile industry market size is forecast to increase by USD 664 million, at a CAGR of 3.2% between 2024 and 2029.

Major Market Trends & Insights

APAC dominated the market and accounted for a 46% growth during the forecast period.
By the Component - Field devices segment was valued at USD 1944.40 million in 2023
By the Solution - Hardware and software segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 22.42 million
Market Future Opportunities: USD 664.00 million 
CAGR : 3.2%
APAC: Largest market in 2023

Market Summary

The automation market in the textile industry is experiencing significant advancements, with numerous applications driving its growth. According to recent reports, the global textile industry automation market size was valued at USD 15.8 billion in 2020, with a steady increase in adoption rates across various sectors. Automation in textile manufacturing enhances productivity, reduces labor costs, and ensures consistent product quality. For instance, the use of automated weaving machines and spinning systems has led to a 20% increase in production efficiency. Moreover, the integration of robotics and artificial intelligence in textile processing has streamlined operations and improved product customization.
The market's continuous evolution reflects the industry's commitment to staying competitive in an increasingly globalized market. As automation technologies continue to advance, we can expect further improvements in production speed, quality, and flexibility.

What will be the Size of the Automation In Textile Industry Market during the forecast period?

Explore market size, adoption trends, and growth potential for automation in textile industry market Request Free Sample

The textile industry is undergoing a significant transformation through automation, enhancing various aspects of production and supply chain management. Current market performance reflects a substantial reduction in textile waste, with approximately 15% of total production being saved through automated material handling and inventory management systems. Looking forward, future growth expectations indicate a potential increase of up to 20% in efficiency gains from the implementation of advanced process control systems and precision engineering textiles. A comparison of key numerical data highlights the impact of automation on textile production. For instance, automated logistics systems have led to a 30% reduction in turnaround times, while supply chain visibility has improved by 45%, ensuring better demand forecasting and predictive modeling capabilities.
The integration of Industry 4.0 technologies, such as smart factories and quality assurance systems, has resulted in enhanced safety and sustainability, with energy efficiency improvements reaching up to 25%. These advancements contribute to the continuous evolution of the textile industry, enabling cost reductions and increased productivity.

How is this Automation In Textile Industry segmented?

The automation in textile industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Component

  Field devices
  Control devices
  Communication


Solution

  Hardware and software
  Services


Technology

  Robotics
  IoT
  AI
  Automation Software


Application

  Spinning
  Weaving
  Dyeing
  Finishing


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Pakistan


  South America

    Brazil


  Rest of World (ROW)

By Component Insights

The field devices segment is estimated to witness significant growth during the forecast period.

The textile industry's automation market is experiencing significant growth, with industrial automation systems becoming increasingly integral to textile production processes. Automated dyeing processes, for instance, have seen a 25% increase in adoption, enabling manufacturers to produce high-quality textiles more efficiently. Similarly, CNC textile machinery and textile design software have gained popularity due to their ability to streamline production and enhance product customization. IoT textile production, 3D textile printing, real-time monitoring textiles, and simulation textile processes are other evolving trends. Smart sensors textiles, SCADA textile integration, and automated cutting systems have witnessed a 17% rise in sales, contributing to enhanced production optimization.

The Field devices segment was valued at USD 1944.40 million in 2019 and showed a gradual increase during the forecast period.

Sewing automation sys

🚀 Data Science Careers in 2025: Jobs and Salary Trends in Pakistan 🚀 Data Science is one of the fastest-growing fields, and by 2025, the demand for skilled professionals in Pakistan will only increase. If you’re considering a career in Data Science, here’s what you need to know about the top jobs and salary trends.

🔍 Top Data Science Jobs in 2025 1) Data Scientist Avg Salary: PKR 1.2M - 2.5M/year (Entry-Level), PKR 3M - 6M/year (Experienced) Skills: Python, R, Machine Learning, Data Visualization

2) Data Analyst Avg Salary: PKR 800K - 1.5M/year (Entry-Level), PKR 2M - 3.5M/year (Experienced) Skills: SQL, Excel, Tableau, Power BI

3) Machine Learning Engineer Avg Salary: PKR 1.5M - 3M/year (Entry-Level), PKR 4M - 7M/year (Experienced) Skills: TensorFlow, PyTorch, Deep Learning, NLP

4)Business Intelligence Analyst Avg Salary: PKR 1M - 2M/year (Entry-Level), PKR 2.5M - 4M/year (Experienced) Skills: Data Warehousing, ETL, Dashboarding

5) AI Research Scientist Avg Salary: PKR 2M - 4M/year (Entry-Level), PKR 5M - 10M/year (Experienced) Skills: AI Algorithms, Research, Advanced Mathematic

💡 Why Choose Data Science? High Demand: Every industry in Pakistan needs data professionals. Attractive Salaries: Competitive pay based on technical expertise. Growth Opportunities: Unlimited career growth in this field.

📈 Salary Trends Entry-Level: PKR 800K - 1.5M/year Mid-Level: PKR 2M - 4M/year Senior-Level: PKR 5M+ (depending on expertise and industry)

🛠️ How to Get Started? Learn Skills: Focus on Python, SQL, Machine Learning, and Data Visualization. Build Projects: Work on real-world datasets to create a strong portfolio. Network: Connect with industry professionals and join Data Science communities.

work_year: The year in which the data was recorded. This field indicates the temporal context of the data, important for understanding salary trends over time.

job_title: The specific title of the job role, like 'Data Scientist', 'Data Engineer', or 'Data Analyst'. This column is crucial for understanding the salary distribution across various specialized roles within the data field.

job_category: A classification of the job role into broader categories for easier analysis. This might include areas like 'Data Analysis', 'Machine Learning', 'Data Engineering', etc.

salary_currency: The currency in which the salary is paid, such as USD, EUR, etc. This is important for currency conversion and understanding the actual value of the salary in a global context.

salary: The annual gross salary of the role in the local currency. This raw salary figure is key for direct regional salary comparisons.

salary_in_usd: The annual gross salary converted to United States Dollars (USD). This uniform currency conversion aids in global salary comparisons and analyses.

employee_residence: The country of residence of the employee. This data point can be used to explore geographical salary differences and cost-of-living variations.

experience_level: Classifies the professional experience level of the employee. Common categories might include 'Entry-level', 'Mid-level', 'Senior', and 'Executive', providing insight into how experience influences salary in data-related roles.

employment_type: Specifies the type of employment, such as 'Full-time', 'Part-time', 'Contract', etc. This helps in analyzing how different employment arrangements affect salary structures.

work_setting: The work setting or environment, like 'Remote', 'In-person', or 'Hybrid'. This column reflects the impact of work settings on salary levels in the data industry.

company_location: The country where the company is located. It helps in analyzing how the location of the company affects salary structures.

company_size: The size of the employer company, often categorized into small (S), medium (M), and large (L) sizes. This allows for analysis of how company size influences salary.

Cross-tabulation of Perception variables with university type.

Correlation matrix of student’s perception of AI and impact of AI in pharmacy education and willingness to use it.

Context The ICC Champions Trophy 2025 is an international One Day International (ODI) cricket tournament organized by the International Cricket Council (ICC), marking the return of this prestigious event after its last edition in 2017. Scheduled to be co-hosted by Pakistan and Dubai, it will feature the top eight teams in the ICC ODI rankings competing in a round-robin and knockout format. Known as the "mini World Cup," the tournament holds significant historical and cultural importance, especially for Pakistan, hosting its first major ICC event since 1996. With matches set in iconic venues across Pakistan and the UAE, the Champions Trophy 2025 promises thrilling encounters and intense rivalries, serving as a prelude to the Cricket World Cup 2027.

Content The dataset includes ODI matches played between 2020 and 2024, featuring teams such as Pakistan, India, Australia, England, South Africa, New Zealand, Sri Lanka, and Bangladesh. It focuses on head-to-head matchups and provides detailed match-level data, including dates, venues, scores, results, batsman averages against specific opponents, and team bowling performance against opposing sides. This dataset offers valuable insights into individual and team performances during this period.

The dataset provides:

Mat: Match HS: Highest Score NO: Not out Ave: Average of Batsmen BF: Balls Faced SR: Strike rate 4s: Fours 6s: Sixes Mdns : Maidens over bowled BBI: Best bowling in Innings Econ: The average number of runs they concede per over they bowl SR: a measure of how quickly a bowler takes wickets, or gets batters out

File Format: Pak vs Ind(Batting) means Pakistan batting vs India,Ind vs Pak (Bowling) means India bowling vs Pakistan and vice verca.

Acknowlegements This dataset belongs to me.I'm sharing it here for free.You may do with it as you wish.