LC International Journal of STEM (ISSN: 2708-7123)

The Impact of Automation and Artificial Intelligence on Employment Dynamics in Pakistan's Manufacturing Sector

Aliza Tabassam — 2024-10-06

The study titled The Impact of Automation and Artificial Intelligence on Employment Dynamics in Pakistan's Manufacturing Sector" was planned to understand the effects of automation and AI on employment in Pakistan's manufacturing sector. The investigation was concerned with the influence of a rising uptake of these technologies in job creation, skill demands, and labor displacement. It also evaluated various industries' technological preparedness levels for AI-led revolution and organizations' and policymakers' competitive and sectoral response. Through the statistical data on employment, the research determined the areas exposed to automation risks – however, the study only provided a math estimate of the number of jobs that could be potentially threatened by automation. The research also raised awareness of re-skilling and the creation of new competencies, especially in emerging technologies such as artificial intelligence, data science, robotics and machine learning. The research highlighted the need for policymakers to intervene, develop viable trade skills and talents for the workforce, and for businesses to get together to reduce job losses and benefit from the opportunities created by automation. The control group study also affirmed, thus, the need for a proactive approach to policy measures designed not only to address the social implications of automation or the changes in the occupational structure towards AI-intensive work but also to support the required strengthening of the links between skills development and the demands of modern manufacturing in Pakistan.

Indoor Smoking Detection Method based on Dual Spectral Fusion Image and YOLO Framework

Abdullah Al Nayeem Mahmud Lavu — 2024-10-06

Indoor fires are a major problem for public safety, with smoking being the most hidden threat. Traditional fire detection systems, such as smoke detectors, are only useful in the early stages and face challenges due to low light and limited visibility. This article describes an indoor smoking detection system that combines visible and infrared image fusion with the YOLO (You Only Look Once) detection framework. This technique improves indoor smoking detection performance by combining infrared thermal data with deep learning concepts. The YOLOv9 system detects indoor smoking behavior using a deep neural network for feature presentation and inference. The approach is optimized at the data, feature extraction, and model training levels to improve scene adaptability. The experimental results showed that on the custom indoor smoking dual spectral fusion image dataset, the average accuracy mAP (@ 0.5) of the Modified YOLOv9c detection model reached 95.8%, which was much better than the baseline models YOLOv5s (81.4%), YOLOv7 (89.7%), YOLOv8 (90.8%), and YOLOv9c (89.9%) mAP, respectively with significant performance improvements. Strategies like dual spectrum fusion, data augmentation, attention mechanism, and loss function were implemented to improve model detection performance. This paper presents a practical solution for indoor smoking detection tasks, demonstrating the approach's superiority in detection performance and providing a viable toolset for public safety against indoor fire hazards.

Enhanced Efficiency and Productivity through AAMS

MD Anisul Islam Jonayed — 2024-10-06

The Traditional attendance management systems, which rely on human operations or RFID-based solutions, frequently struggle with scalability, accuracy, and efficiency. This thesis proposes an Automated Attendance Management System (AAMS) that employs a customized YOLOv9-C model for real-time facial recognition via deep learning. The model's performance is significantly improved by adding Squeeze-and-Excitation (SE) blocks and the Complete Intersection over Union (CIoU) loss function. On a custom dataset, the baseline YOLOv9-C model had 86.2% precision and 84.9% recall, with a mean Average Precision (mAP) of 89.9% at IoU threshold of 0.5. However, the revised YOLOv9-C(M) model demonstrated significant gains, including a mAP of 93.8%, as well as improved precision (94.1%) and recall (96.6%).

These improvements can be due to the introduction of SE blocks, which promote feature recalibration, and the CIoU loss function, which maximizes bounding box localization and increases detection accuracy even in tough conditions such as occlusion or dimly lit areas. The improved YOLOv9-C model consistently outperforms the existing YOLO models (YOLOv5, YOLOv7, and YOLOv8s), according to a comparison study. The mAP for YOLOv5 was 80.2%, YOLOv7 was 89.1%, and YOLOv8s was 91.4%. In contrast, the upgraded YOLOv9-C model outperformed the others, with greater robustness, precision, and recall.

The system employs a one kind of custom dataset to evaluate the model's performance in some scenarios and settings, as well as to ensure trustworthy workforce detection in diverse contexts. By automating the attendance process, this technology reduces errors, saves administrative time, and promotes institutional efficiency.

Research Comparative Analysis of OCR Models for Urdu Language Characters Recognition

Muhammad Murad — 2024-10-06

There have been many research works to digitalize Urdu Characters through machine learning algorithms. The algorithms that were already used for Urdu Optical Character Recognition [OCR] are Convolutional Neural Network [CNN], Recurrent Neural Network [RNN], and Transformer etc. There are also many machine learning algorithms that have not been used for Urdu OCR e.g Support Vector Machine, Graph Neural Network etc. This research paper proposes a comparative study between the performances of the already implemented Urdu OCR on some of following algorithms like Convolutional Neural Network/ Transformer Model it also proposed a new implemented Urdu OCR using on Support Vector Machine algorithm.

Exploring Data-Driven Approach for Financial Fraud Detection: A Comprehensive Literature Review

Qayoom Abdul — 2024-10-06

Financial fraud detection has emerged as a critical area of research with the growing complexity and scale of fraudulent activities in the financial sector. Traditional methods of fraud detection, which are based on rule-based systems and manual oversight, fail to capture the dynamic and sophisticated nature of modern fraud schemes. This comprehensive literature review examines data-driven approaches that take into account the advancement of machine learning, artificial intelligence, and big data analytics to improve fraud detection. Some of the key methodologies covered are supervised, unsupervised, and hybrid models. The survey reflects growing usage in neural networks, ensemble methods, and anomaly detection techniques, emphasizing their performance in identifying complex fraud patterns in different financial datasets. Discussions include the difficulties with unbalanced datasets, evolving tactics for frauds, and requirements for explainability that remain future areas of interest. Drawing upon recent relevant research work, this review synthesis aims at informing readers concerning the landscape evolution in fraud detection against finances and presenting possible innovations in order for these to remain robust yet adaptive, clear, and transparent in nature.