Spectrum of Engineering Sciences

DESIGN & IMPLEMENTATION OF SMART GRID USING CELLULAR NETWORKS AS ITS AMI BACKBONE

2025-05-02T12:29:55+03:00

To solve the limitations of current power grid, Smart Grid is the solution. Its backbone is AMI (Advanced Metering Infrastructure), but its major problem is lack of unified data transmission infrastructure within the grids which should able to send big amount of data collected from each smart meter of a town or city to the center in cheap and secure way. In the current design of AMI in SG needs the implementation of a separate/mix transmission data infrastructure which will have very high cost. So, we introduce the use of Cellular networks in transmitting consumer data (usage and controlling) to the center and vice versa. We have designed a system where smart meters equipped with Cellular SIM Cards and Wi- Fi technologies communicate directly with the Hub in a secure way, bypassing the need for extensive infrastructure. The Wi-Fi in smart meters will be used in IoT of customer's home equipment management. This system could be deployed in the whole country just by installing smart meters and renting Cellular Companies Transmission infrastructure to send each customer data to the Hub. This design has been simulated in MATLAB. So, this approach associated with low-cost, high security and easy maintenance as the Cellular network's companies will be responsible for its maintenance just like their new cellular sites. This re-search provides a blueprint for utility companies and policymakers to upgrade electricity systems economically and efficiently using/renting already installed Cellular infrastructure.

INNOVATIVE ARCHITECTURE AND INDUSTRIALIZATION BUILDING SYSTEM IN THE CONSTRUCTION INDUSTRY OF PAKISTAN

2025-05-02T12:38:21+03:00

Industrialized Building System (IBS) construction has emerged as a sustainable method for enhancing productivity and mitigating the adverse environmental and social impacts associated with conventional construction practices. While a growing body of research has addressed management issues in prefabricated construction, a systematic summary and strategic framework for its optimization remain underdeveloped. This study aims to develop a framework for optimizing the use of industrialized building construction by integrating innovative architectural approaches and construction waste reduction strategies. Through a comprehensive review of best practices in prefabrication implementation, including adoption rates, construction methods, cost-effectiveness, and performance evaluation, this paper examines effective implementation strategies, industry prospects, and the enabling environment for technological application. Special focus is placed on design, production, transportation, and assembly processes. The findings offer an improved understanding of the critical factors influencing the success of IBS and provide practical guidance for enhancing future adoption, promoting sustainability, and advancing innovation within the construction industry.

HIGH-PERFORMANCE LINEAR TO LINEAR POLARIZATION CONVERTER EXHIBITING 30° ANGULAR STABILITY

2025-05-02T12:46:21+03:00

A thin linear polarization converting metasurface has been proposed with better efficiency and performance. The proposed surface is etched on 3mm thick dielectric substrate. It has the ability to convert linear to orthogonal equivalent in the 10.5-21.5GHz band with polarization conversion ratio above 90%.along with this, parametric analyses has also been conduct to monitor the effect of various parameter on the performance of surface. Moreover, for deeper insight into polarization conversion mechanism the surface current distribution is carried out.

ENHANCED BRAIN TUMOR DETECTION IN MRI: A COMPARATIVE STUDY OF MACHINE LEARNING MODELS

2025-05-02T13:11:32+03:00

Image processing is essential and attractive in the medical and healthcare. Digital image processing identifies diverse pathological methods, like identifying, classifying, evaluating, and testing brain tumors through microscopic images. Many machine-learning methods are recognized in the era of the AI century for detecting brain tumors through Magnetic Resonance Imaging (MRI). MRI is a recognized image processing method through three-dimensional examination, which identifies unambiguous images of the infection or tumor. The paper aims to offer supervised machine-learning algorithms for brain tumor detection in MRI images through a comparative analysis of different models. Considering the specific features of the tumor and surrounding infected tissues of the brain through analysis supports us in estimating the accuracy of the models and recognizing the optimal operative method. In this paper, four supervised machine learning models are considered: Logistic Regression (LR), Neural Network (NN), Stochastic Gradient Descent (SGD), and Support Vector Machines (SVM). MRI images can quickly identify brain tumors or infections by comparing these models. Furthermore, a model is developed using the Visual Geometry Group (VGG-19) embedder and the Kaggle dataset. The result section shows that the proposed model outperforms the benchmark schemes by attaining high proximity accuracies.

ANDROID MALWARE ANALYSIS USING ARTIFICIAL INTELLIGENCE

2025-05-03T08:49:38+03:00

Mobile phones have become a crucial part of society and serve as more than just communication devices. The growing use of smartphones has led to a large number of apps, making it difficult for app marketplaces to validate their legitimacy. Conventional security solutions for computer malware are challenging to apply on mobile devices due to different resource management mechanisms. Implementing intelligent tools using the Machine Learning in the threat identification process of security software can improve its efficiency by analyzing data and identifying potential threats. This reduces the need for human intervention and allows for faster detection of risks, saving time and resources. Intelligent tools can also continuously monitor data and identify potential threats in real-time, further improving the threat identification process. In conclusion, the use of intelligent tools can significantly enhance the effectiveness of conventional security software and protect against potential threats. This can help prevent hacking and data theft and keep personal information safe and secure. Additionally, these intelligent tools can be easily integrated into current security systems, making it easy for organizations to improve their overall security posture.

SMART FILTERS FOR SMS SPAM: A MACHINE LEARNING APPROACH TO SMS CLASSIFICATION

2025-05-03T09:02:59+03:00

The exponential rise in the number of undesired text messages delivered via SMS has been directly related to the explosion in the number of mobile phones sold. Although various information channels are considered "spotless" and trustworthy in many parts of the world, ongoing reports show that cell phone spam is significantly increasing. It is a big problem. It is becoming increasingly pervasive worldwide, especially in Asia and the Middle East. In the same way that finding a solution to such an issue can be time-consuming, so can the process of identifying spam texts from genuine communications. It solves many difficulties and makes life much easier because it can distinguish between real SMS and spam. In any event, it faces specific challenges and obstacles that are unique to itself. During this current research, we have investigated five Machine Learning (ML) methods to identify spam in a short text message using a single dataset containing SMS spam Collection. The SMS spam dataset was extracted from the Kaggle repository. The experiment is carried out on the R platform. Eleven characteristics, including binary and numeric features like Char Count, Has number, Has URL, Has Date, Has dollar, Emoticon, Email, and Phone, as well as spam count, ham count, and spam binary, are employed in this research. These features are used for feature selection and showing results using Machine Learning(ML) approaches. The effectiveness of the various strategies or methods is evaluated using metrics such as sensitivity, accuracy, precision, F1 score, recall, and specificity. The outcomes show that the light gradient boosting machine (LGBM) with these features achieved a sensitivity score of 100, precision score of 100, F1 score of 100, recall of 100, and specificity score of 100, with an optimal accuracy score of 100 percent, which is outstanding compared to all other state-of-the-art studies.

ASSESSING THE AGRONOMIC AND ECONOMIC VIABILITY OF DISTILLERY SPENT WASH AND BOILER ASH MIXTURES AS SUSTAINABLE FERTILIZERS

2025-05-03T19:06:48+03:00

A significant by-product of the sugar industry is molasses, which distilleries utilize for alcohol production through fermentation. This process generates 10 to 15 cubic meters of wastewater for every cubic meter of alcohol produced. Common disposal methods for this waste effluent include fertilization, irrigation, composting with bio-waste, combustion, and anaerobic treatment. Rich in organic matter, mineral salts, and essential nutrients, this effluent can be efficiently repurposed as a nutrient source and soil amendment. By adopting these methods, industries can enhance soil fertility while managing waste responsibly, contributing to sustainable agricultural practices. Research studies have demonstrated that the nutrient content (NPK) in distillery sludge makes it suitable for use as a fertilizer. A developed fertilizer combining distillery spent wash (DSW) and boiler ash (BA) was tested on cotton fields, resulting in improved crop growth, higher yields, and reduced costs. This approach enhances nutrient availability and offers a viable alternative to synthetic fertilizers for agricultural productivity. A parametric study was conducted to evaluate the effectiveness of this low-cost method, focusing on the utilization of waste materials like DSW and BA from the alcohol industry as sustainable fertilizers. The findings highlight the potential of these waste-derived fertilizers to improve agricultural outcomes while addressing environmental and economic challenges, paving the way for more sustainable industrial and farming practices.