Papers by Md Ashfakul Karim Kausik
ELSEVIER Hybrid Advances, 2025
This review paper explores the dynamic landscape of precision agriculture and the integration of ... more This review paper explores the dynamic landscape of precision agriculture and the integration of Unmanned Aerial Vehicles (UAVs) with Artificial Intelligence (AI) and the Internet of Things (IoT). Precision agriculture has emerged as a transformative approach to farming, enabling data-driven decisions, resource optimization, and improved yields. UAVs have become essential tools in agriculture, providing real-time data on crop health, pest infestations, and irrigation needs. Integrating AI and IoT further enhances precision agriculture by enabling data analytics, predictive modeling, and remote monitoring. This paper delves into the concepts and techniques of precision agriculture, the role of UAVs in farming, the applications of AI in crop management, and the IoT devices and connectivity protocols used in agriculture. It highlights successful case studies where UAVs, AI, and IoT work synergistically to deliver efficient, data-driven solutions. The paper also addresses these technologies' challenges and ethical concerns and discusses future trends and innovations. Integrating UAVs, AI, and IoT represents a promising avenue for revolutionizing farming practices, improving efficiency, and ensuring sustainable and environmentally sensible agriculture.
ELSEVIER Array, 2025
Adopting Machine Learning (ML) in manufacturing quality assurance (QA) has accelerated with Indus... more Adopting Machine Learning (ML) in manufacturing quality assurance (QA) has accelerated with Industry 4.0, enabling automated defect detection, predictive maintenance, and real-time process optimization. However, selecting the most effective ML model remains challenging due to performance variability, scalability constraints, and inconsistent evaluation metrics across manufacturing sectors. This systematic review analyzes over 300 peerreviewed studies over the last two decades (mostly analyzing the recent works) to evaluate the effectiveness of widely used ML algorithms-Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Random Forests (RFs), Decision Trees (DTs), and K-Nearest Neighbors (KNN)-in QA applications. Performance metrics include accuracy, precision, speed, recall, computational efficiency, scalability, and real-time processing capabilities. Findings reveal that ANNs outperform other models in image-based defect detection, while SVMs and RFs excel in predictive maintenance and process parameter optimization. DTs provide better interpretability for process control, and KNN is effective for small-scale QA implementations. In specific case scenarios, RF models showed particular strength in handling high-dimensional sensor data in fault detection in manufacturing quality assurance operations. The study presents a comparative assessment framework, guiding algorithm selection based on industry-specific requirements and operational constraints. This review provides the latest implementation of ML in QA along with quantitative evidence on which algorithm offers the most optimization in specific industrial settings, which would help in algorithm selection in manufacturing quality assurance in future for both researchers and industrial experts. Also, it offers an overview of the major and minor algorithms based on their performance metrics.
ELSEVIER Hybrid Advances, 2025
Out of many threats, plant diseases are the major ones to agriculture globally. They can drastica... more Out of many threats, plant diseases are the major ones to agriculture globally. They can drastically reduce productivity and lead to substantial economic losses. Traditional disease detection methods around these areas are often time-consuming, costly, and less effective, leading to the exploration of advanced techniques such as deep learning. In this study, we compared the results of three different deep learning approaches, namely VGG16, Inception v3, ResNet, and a custom CNN model for the detection of plant diseases in the context of tropical regions. To evaluate the performance of each approach, we used a dataset consisting of images of cauliflower plant diseases commonly found in countries like Bangladesh, India, and others. We trained each model using a transfer learning approach, where we used pre-trained models initially trained on the VegNet dataset on various train-validation splits. Various evaluation metrics were used to conduct this study: accuracy, precision, loss, recall, and F1 score. The ResNet50 model performed the best with an accuracy of 90.85 %, followed by our proposed model with an accuracy of 89.04 %. The findings suggest that deep learning approaches, especially Resnet50, and the proposed model can effectively detect diseases in tropical regions. The study's results suggest that using advanced technologies, such as deep learning, can significantly enhance the effectiveness of disease detection and control, leading to improved agricultural productivity and food security.
Artifcial intelligence (AI) has become a reality in today's world with the rise of the 4th indust... more Artifcial intelligence (AI) has become a reality in today's world with the rise of the 4th industrial revolution, especially in the armed forces. Military AI systems can process more data more efectively than traditional systems. Due to its intrinsic computing and decision-making capabilities, AI also increases combat systems' self-control, self-regulation, and self-actuation. Artifcial intelligence is used in almost every military application, and increased research and development support from military research agencies to develop new and advanced AI technologies is expected to drive the widespread demand for AI-driven systems in the military. Tis essay will discuss several AI applications in the military, as well as their capabilities, opportunities, and potential harm and devastation when there is instability. Te article looks at current and future potential for developing artifcial intelligence algorithms, particularly in military applications. Most of the discussion focused on the seven patterns of AI, the usage and implementation of AI algorithms in the military, object detection, military logistics, and robots, the global instability induced by AI use, and nuclear risk. Te article also looks at the current and future potential for developing artifcial intelligence algorithms, particularly in military applications.
Artificial Intelligence (AI) technology's rapid advancement has significantly changed various ind... more Artificial Intelligence (AI) technology's rapid advancement has significantly changed various industries' operations. This comprehensive review paper aims to provide readers with a deep understanding of AI's applications & implementations, workings, and potential impacts across different sectors. It also discusses its future, threats, and integration into new policy. Through extensive research on more than 200 research and many other sources, the authors have made every effort to present an accurate overview of the numerous applications of AI nowadays in industries such as agriculture, education, autonomous systems, healthcare, finance, entertainment, transportation, military, manufacturing, and more. The paper explores various AI technologies, including machine learning, deep learning, robotics, big data, the Internet of Things, natural language processing, image processing, object detection, virtual reality, augmented reality, speech recognition, and computer vision. It provides realworld examples of their applications and implementations. Moreover, it highlights and evaluates the future potential, challenges, and limitations associated with the widespread use of AI. Our study incorporates the latest research to offer a comprehensive and nuanced understanding of AI's potential benefits and challenges. This data-driven review case study highlights the immense potential of AI technology and addresses the ethical, societal, and economic considerations related to its implementation.
Uploads
Papers by Md Ashfakul Karim Kausik