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All Journal ASEAN Journal of Community Engagement JATAED: Journal of Appropriate Technology for Agriculture, Environment, and Development PERFECT: Journal of Smart Algorithms
Raza, Hassan
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Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering Public Health Social Sciences Other

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Social Justice and Community Development: A Multilevel Community Engagement Model to Effectively Work with Families Living in Culturally-Diverse Communities in Pakistan Raza, Hassan
ASEAN Journal of Community Engagement Vol. 5, No. 1
Publisher : UI Scholars Hub

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Abstract

The current paper introduces the Multilevel Community Engagement Model (MCEM) to help development agencies effectively work with families living in diverse communities in Pakistan. This model is grounded in family systems theory, participatory action research (PAR), and ecological systems theory. It is also informed by three empirical studies and the author’s reflections of his direct observations and experiences based on his work with development agencies. The MCEM uncovers important insights about the complex, dynamic, and reciprocal interactions among different groups of stakeholders at three different engagement levels (i.e., proximal, influential, and holistic). MCEM emphasizes a strong collaboration, effective coordination, and open communication among stakeholders within and between these levels. Development agencies can use and apply MCEM, which may help them adequately understand the needs of families living in diverse communities and address those needs in socially just manners. Additionally, MCEM honors community voice and encourages local knowledge, which may magnify the efforts of stakeholders’ groups who are involved in the community development process and situated within/between three different engagement levels and ensure the successful sustainability of development projects in Pakistan. Although, this model is grounded in research, which was carried out in Pakistan, it is intended to be adapted such that it can be transformed and applied in other countries/societies/cultures. The implications and limitations of MCEM are discussed.
AI and Data Analytics for Precision Agriculture: Current Progress and Future Directions Jamal, Ahmad; Raza, Hassan; Erdenetsogt, Tsendayush; Singh, A; Farooq, Mazhar; Kabeer, Muhammad Mohsin; Aslam, Muhammad Shahrukh
JATAED: Journal of Appropriate Technology for Agriculture, Environment, and Development Vol. 2 No. 2 (2025): JATAED: Journal of Appropriate Technology for Agriculture, Environment, and Dev
Publisher : LEMBAGA KAJIAN PEMBANGUNAN PERTANIAN DAN LINGKUNGAN (LKPPL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62671/jataed.v2i2.88

Abstract

The application of artificial intelligence (AI) and data analytics to support farming operations and ensure a higher complexity of farming practices is what underlies precision agriculture with the purpose of promoting sustainability, higher productivity, and optimization of farming practices. Through the combination of sensor, drone, and satellite data, along with the use of IoT devices, AI-driven systems enable real-time monitoring, prediction, and decision-making. Its current applications are crop monitoring, yield prediction, pest and disease detection, soil nutrient management and optimization of irrigation. Despite the challenges of high costs, data constraints, and technological hurdles, new trends such as edge AI, digital twins, autonomous machinery, and climate-smart solutions will enable widespread adoption. The present review indicates the recent advances, issues, and perspectives of AI-enabled precision agriculture.
A Comprehensive Review on Data Science Frameworks for Big Data Analytics Raza, Hassan; Erdenetsogt, Tsendayush; Singh, A; Farooq, Mazhar; Kabeer, Muhammad Mohsin; Aslam, Muhammad Shahrukh
PERFECT: Journal of Smart Algorithms Vol. 3 No. 1 (2026): PERFECT: Journal of Smart Algorithms, Article Research January 2026
Publisher : LEMBAGA KAJIAN PEMBANGUNAN PERTANIAN DAN LINGKUNGAN (LKPPL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62671/perfect.v3i1.217

Abstract

The importance of big data analytics is now essential in deriving insights in large and complex information in various industries. This review discusses major data science frameworks, such as Apache Hadoop, Spark, Flink, and Storm, their architecture, capabilities, and a relative advantage of processing batches and in real-time. It also presents major challenges that can affect the framework efficiency, including scalability, latency, and heterogeneity of data, security, and the complexity of operational, among others. Lastly, the new trends such as the adoption of AI, cloud-native architecture, real-time streaming, and intelligent automation are discussed to demonstrate the changing environment. This review gives an in-depth insight into the concept of big data frameworks and how they facilitate the achievement of effective analytics.
Machine Learning Driven Decision Making in the Modern Data Era Raza, Hassan; Singh, A; Erdenetsogt, Tsendayush; Kabeer, Muhammad Mohsin; Aslam, Muhammad Shahrukh; Farooq, Mazhar
PERFECT: Journal of Smart Algorithms Vol. 3 No. 1 (2026): PERFECT: Journal of Smart Algorithms, Article Research January 2026
Publisher : LEMBAGA KAJIAN PEMBANGUNAN PERTANIAN DAN LINGKUNGAN (LKPPL)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62671/perfect.v3i1.224

Abstract

The era of modern data has seen an unprecedented increase in the number of data generated, which generates an opportunity as well as a challenge to the decision making. Machine learning (ML) has become the significant solution to work with big and multifaceted data, recognize trends, and provide foresight and change the usual decision-making processes. This review examines the principles, methods and uses of ML-based decision systems in various industries, such as healthcare, finance, retail, transportation and education. It also analyses problems of data quality, bias, transparency, ethical considerations and developments related to explainable and trustworthy AI. Lastly, future trends, human-machine cooperation, and research perspectives are addressed, with a focus on the possibility of the ML to accelerate, more precise and answerable decisions in the world that runs on data.
Co-Authors A Singh Ahmad Jamal Aslam, Muhammad Shahrukh Erdenetsogt, Tsendayush Farooq, Mazhar Kabeer, Muhammad Mohsin