Pakistan Journal of Scientific Research

Enhancing the Properties of Subgrade Soil: Investigation from the Highway Project

Thu, 22 Feb 2024 00:00:00 +0000

The present investigation focused on soil stabilization methodologies expected to improve the properties of subgrade soil while pavement building. The subgrade layer, located beneath the base or surface course of the pavement, often consists of weak soil that lacks the necessary strength to support pavement loading. Effective soil stabilization is essential to ensure long-term performance and reduce pavement distress such as cracking and rutting. Various soil stabilization methods have been explored, including mechanical, cement, lime, bitumen, electro-osmosis, thermal, and chemical stabilization. Researchers have investigated using different materials and techniques to enhance subgrade soil properties. The application of soil stabilization techniques in Pakistan remains limited, highlighting the need for further research and implementation in highway projects. This Study provides valuable insights into soil stabilization methods and their potential benefits for subgrade soil improvement in pavement construction.

CONVERSATIONAL ASSISTIVE TECHNOLOGY OF VISUALLY IMPAIRED PERSON FOR SOCIAL INTERACTION

K Ghafoor, T. Ahmad, M. Hanif, H. Zaheer — Fri, 01 Dec 2023 00:00:00 +0000

Assistive technology has been developed to support visually impaired people in their social interactions. Conversation assistive technology is designed to enhance communication skills, facilitate social interaction, and improve the quality of life of visually impaired individuals. This technology includes speech recognition, text-to-speech features, and other communication devices that enable users to communicate with others in real-time. The technology uses natural language processing and machine learning algorithms to analyze spoken language and provide appropriate responses. It also includes features such as voice commands, and audio feedback to provide users with a more immersive experience. These technologies have been shown to increase the confidence and independence of visually impaired individuals in social situations, and have the potential to improve their social skills and relationships with others. Overall, conversation-assistive technology is a promising tool for empowering visually impaired people and improving their social interactions. One of the key benefits of conversation-assistive technology is that it allows visually impaired individuals to overcome communication barriers that they may face in social situations. It can help them to communicate more effectively with friends, family, and colleagues, as well as strangers in public spaces. By providing a more seamless and natural way to communicate, this technology can help to reduce feelings of isolation and improve overall quality of life. The main objective of this research is to give blind users the capability to move around in unfamiliar environments, through a user-friendly device by face, object, and activity recognition system. This model evaluates the accuracy of activity recognition. This device captures the front view of the blind, detects the objects, recognizes the activities, and answers the blind query. It is implemented using the front view of the camera. The local dataset is collected that includes different 1st-person human activities. The results obtained are the identification of the activities that the VGG-16 model was trained on, where Hugging, Shaking Hands, Talking, Walking, Waving video, etc.

COMPARATIVE ANALYSIS OF LOSSY IMAGE COMPRESSION ALGORITHMS

umer Ejaz, F. Y. Khuhawar, I. Bari, A. Ijaz, A. Iqbal, F. Gillani, M. Hayat — Fri, 01 Dec 2023 00:00:00 +0000

The demand for efficient image storage and transmission has driven extensive research into lossy image compression algorithms. This paper presents a comprehensive comparative analysis of three prominent lossy image compression techniques: Discrete Cosine Transform (DCT), Wavelet Transform, and Vector Quantization (VQ). Using a diverse dataset and various evaluation metrics including Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Bitrate, and Computational Complexity, we assess their performance in terms of image quality, compression efficiency, and computational demands. Our findings reveal that DCT excels in preserving image quality, closely followed by Wavelet Transform. VQ, while efficient in compression, lags in image quality preservation. Based on the comparative analysis of three key lossy image compression algorithms, it was observed that DCT stands out as the most appropriate technique to consider for applications that prioritize image quality preservation. It offers high Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) scores, indicating superior image fidelity. While it may not be the most computationally efficient, DCT strikes a balance between compression efficiency and image quality.

Classification of Isocitrate Dehydrogenase (IDH) Mutation Status in Gliomas Using Transfer Learning

Maria Fayyaz, NaumanRiaz Chaudhry, Reema Choudhary — Tue, 16 Apr 2024 00:00:00 +0000

Isocitrate Dehydrogenase (IDH) mutation is a significant genetic alteration that is found in brain tumors. Its diagnosis is vital for the prognosis of low-grade Glioma and secondary grade Glioma patients. Physicians used invasive methods to diagnose the Gliomas, which was an unsafe method but now advanced magnetic resonance imaging techniques are being used for tumor visualization and treatments. Classical machine learning and deep learning methods have been used by some studies for the problem of IDH mutation status detection using magnetic resonance images. Recent studies have used concatenation of deep and handcrafted features to achieve superior performance. This study has used the concatenations of the deep features extracted through pre-trained convolution neural networks (CNNs) for the task of IDH mutation status detection using magnetic resonance images. To select the pre-trained CNNs, five top accuracies on the ImageNet dataset were considered. Magnetic resonance images were acquired from The Cancer Genome Atlas Glioblastoma Multiforme and The Cancer Genome Atlas Low-Grade Glioma. All experiments (performed using features extracted from each CNN and their concatenation) were compared with each other and state-of-the-art. The proposed technique achieved 99% accuracy while being efficient in terms of data and computational resources.

MACHINE LEARNING TECHNIQUES FOR IDENTIFYING SELF-CARE PROBLEMS IN DISABLED CHILDREN

ubchaudry, Amna Wajid — Thu, 22 Feb 2024 00:00:00 +0000

The identification and intervention of self-care problems in disabled children are crucial for enhancing their quality of life and independence. The utilization of machine learning algorithms holds promise in revolutionizing the identification and handling of these self-care challenges potentially offering tailored solutions to improve the well-being and autonomy of disabled children. Therefore, a literature review is imperative to comprehensively assess the landscape of machine learning (ML) applications in addressing these self-care challenges. Existing SLRs on this topic lack comprehensive coverage of ML-based techniques, hindering a full understanding of their efficacy in classifying self-care issues among disabled children. This review aims to assess that how ML methodologies contribute to identify and address these challenges along with their impacts on accuracy and clinical relevance. By encapsulating various ML methodologies used in diagnosing selfcare problems, this review reveals their diverse impacts on accuracy and clinical applicability. The novel aspect of this work lies in the comprehensive coverage and evaluation of diverse ML techniques, highlighting their potential to transform pediatric healthcare for disabled children. In conclusion, this review demonstrates that hybrid ML models, feature selection and extraction techniques significantly enhance classification accuracy paving the way for improved interventions. This comprehensive analysis makes this review a valuable resource for researchers seeking insights into ML's role in addressing self-care challenges among disabled children.

DESIGNING AN INVENTORY MANAGEMENT SYSTEM USING DATA MINING TECHNIQUES

A. A. Malik, K. Amjad, A. Saud, A. Ali — Fri, 01 Dec 2023 00:00:00 +0000

Designing a POS system that enables the FBR to keep a record of the transactions made at the Tier-1 retailers who fall under the tax ruling of FBR. This will enable the FBR to check how much tax is being collected from the consumer. With that record, they will be able to estimate how many people are filled and what it enables to collect tax from the retailers with the help of FBR invoice number that will generate in real-time when the receipt is generated for the shopping consumer. Datamining techniques are effective in uncovering the previously hidden patterns for storing the data these techniques can be applied in every field such as banking, agriculture, and marketing. Several different data mining techniques have been used to improve the performance and predictions of exciting data. The novelty of the proposed system is that different data mining techniques get time stamps on yearly and monthly based transactions which gives us different variations-based graphs with precise working of data mining techniques.