International Journal of Innovations in Science & Technology
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Analyzing Privacy in Frank Lloyd Wright\u27s Prairie Style Homes Through Syntactic Methods using “A Graph” and Depth Map X Softwares
Get PDFFrank Lloyd Wright\u27s Prairie Style homes, designed across the United States, showcase his unique architectural approach. This study examines how Wright\u27s designs interact with environmental conditions, focusing on privacy in eight Prairie Style homes. Detailed floor plans and architectural evaluations were analyzed using space syntax tools to assess spatial connections. The results show that Wright prioritized bedroom privacy, with lower integration values indicating seclusion. Public areas like living and dining spaces had higher integration values, promoting connectivity. The study confirms that bedrooms in Wright\u27s Prairie Style homes are intentionally designed as private spaces, with some exceptions. These findings highlight the importance of layout morphology in creating private zones within open-plan layouts. This research sheds light on Wright\u27s innovative approach to balancing privacy and openness in residential architecture
Design and Analysis of Microstrip Patch Antenna Operating at Higher Order Mode
Get PDFThis paper proposes an enhanced bandwidth microstrip patch antenna by exciting it with higher order modes. Characteristics Mode Analysis (CMA) is used to analyze and understand the possible modes for bandwidth enhancement of microstrip patch antenna. Furthermore, Defected Ground Structure (DGS) technique is utilized for bandwidth enhancement. The proposed antenna is having a size of 67.5 × 67.5 mm2 with an operating frequency of 5.8 GHz. The impedance bandwidth is increased by 13.8% using Defected Ground Structure (DGS) by adding slots in the ground for higher order mode operation. Moreover, the proposed antenna has an overall efficiency of above 80. Therefore, enhanced impedance bandwidth, improved radiation pattern, and compatible design make the design novel and suitable for practical wireless applications
Dynamic of Land Use Land Cover and its Impact on Land Surface Temperature (LST) Using GIS: A Study of District Mardan, Pakistan
Get PDFRapid population growth is a global issue that alters landscapes and affects environmental conditions. This study aims to assess the effects of urbanization on Land Use Land Cover (LULC) changes and their impact on Land Surface Temperature (LST) in District Mardan from 2002 to 2022. By combining remote sensing data on LULC changes with LST measurements, researchers can analyze the relationship between these variables. This analysis sheds light on how LULC changes affect local climate patterns, urban heat island effects, and the overall thermal environment. Data preprocessing was done using ArcGIS 10.8 software. After preprocessing, a supervised classification scheme was applied for 2002 and 2022, using the maximum likelihood algorithm to identify LULC changes. In 2002, the built-up area covered 165.47 km², which increased to 266.70 km² by 2022. Vegetation covers gradually declined over the same period, with a notable shift from vegetation to built-up areas. The study revealed significant changes in District Mardan, including population growth, urban expansion, and infrastructure development. These changes were influenced by various factors, including land cover types. The results of this study can be useful for regional and urban planning, as well as for managing agricultural practices in the future
Analyzing the Contribution of Transportation in Formation of Smog in District Lahore, Pakistan
Get PDFIntroduction: Globally, smog has become a growing environmental issue, largely due to rising pollution from both anthropogenic and natural sources. In Lahore, a densely populated city in Pakistan, this problem is intensified by rapid urbanization, transportation emissions, and activities related to industry and agriculture.
Novelty: This research highlights these factors as primary contributors to smog and utilizes a quantitative approach to evaluate their impacts, particularly focusing on the role of transportation in smog formation.
Methodology: The study examines data on vehicular emissions, traffic patterns, and meteorological conditions to explore the relationship between transportation activities and smog. Data collection was performed via an online questionnaire survey. This data was then mapped onto traffic congestion areas and major industrial pollution sources, with charts created to depict public perceptions of the issue.
Results and Discussion: The results show that vehicles are a significant source of smog, emitting NOx, VOCs, and particulate matter (PM). Older vehicles, especially those lacking modern pollution control technologies, emit far more pollutants per kilometer than newer models that adhere to stringent emission standards. Traffic congestion during peak hours leads to extended vehicle idling, which increases fuel consumption and emissions. Additionally, stop-and-go traffic conditions further heighten fuel use and pollutant release.
Concluding Remarks: Tackling transportation-related smog is essential and necessitates effective management and policies at the local, regional, and national levels. Public-private partnerships can be instrumental in advancing cleaner technologies and practices within the transportation sector to mitigate smog
Spatial Assessment of Atmospheric Contamination and Urban Heat Phenomenon in Urban Centers of Sindh, Pakistan
Get PDFRapid urbanization and industrialization in Sindh, Pakistan, particularly in urban centers like Karachi, have led to significant challenges related to air pollution and climate change. This study uses Geographic Information Systems (GIS) to analyze air quality indices (AQI) in Sindh, focusing on particulate matter such as PM2.5. It offers a novel approach by visually mapping the spatial patterns and potential correlations between air pollution and temperature, a topic not extensively covered before. Through interpolation methods and temporal graphing of AQI values, the study identifies areas with high air pollution and examines their spatial distribution throughout the year. The comparison of PM2.5 concentrations with land surface temperature (LST) maps reveals patterns where higher pollution levels often align with urban centers, intensifying the urban heat island effect due to excess heat generated by human activities. Beyond impacting human health, air pollution affects ecosystems, soil, water, and biodiversity. The study highlights how areas with significant air pollution tend to have higher surface temperatures, indicating a direct link between pollution and temperature increases. However, the relationship is complex, as the effects of air pollution on climate are influenced by factors like geographic location, meteorological conditions, and pollutant composition. This research provides valuable insights into the spatial dynamics of atmospheric contamination and its implications for urban heat formation in Sindh, enhancing the understanding of how human activities, air quality, and climate change interact
Medical Intent Classification Using Ensemble and Deep Learning Models
Get PDFIntroduction: Medical chatbots are innovative solutions that leverage Natural Language Processing (NLP) and Artificial Intelligence (AI) to enhance communication efficiency between healthcare providers and patients. In the realm of conversational AI, intent classification—the task of understanding a user\u27s intent from natural language input—is both a complex and crucial aspect of the technology. This process is vital for ensuring that chatbots can accurately interpret and respond to patient queries in a meaningful and contextually appropriate manner.
Novelty Statement: This research proposes a hybrid approach that combines transformer-based embeddings with traditional deep learning models to reduce both complexity and computational cost in medical intent classification. By integrating the strengths of advanced transformer techniques with more established models, this approach aims to improve efficiency without sacrificing performance, making it more suitable for real-world healthcare applications.
Material and Method: This study investigates the use of context-aware word embeddings, including word2vec and sentence transformers, to capture rich semantic information from medical text. To refine the unstructured data, we apply various NLP preprocessing techniques, such as text cleaning, stop word removal, and lemmatization. For classification, we utilize a combination of ensemble-based and deep learning methods, including XGBoost, Random Forest, LSTM, and Bi-LSTM. These methods are tested on real-world data from 6,662 patients, with the dataset containing 25 distinct classes.
Result and Discussion: Empirical analysis demonstrates that the Bi-LSTM model, when combined with sentence transformers, achieves an accuracy of 95.23%, outperforming state-of-the-art models reported in the relevant literature.
Concluding Remarks: This research is expected to be highly beneficial to healthcare professionals by enhancing information extraction and enabling more effective handling of patient queries
Explicit State Model Checking Effects on Learning-Based Testing
Get PDFExploring the impact of integrating an explicit state model checker into the learning-based testing (LBT) framework presents an intriguing challenge. Traditionally, LBT has leveraged symbolic model checkers such as NuSMV and SAL, which use Binary Decision Diagrams (BDDs) to analyze multiple states concurrently. In contrast, explicit state model checkers evaluate one state at a time, a key distinction that suggests potential advantages for explicit state checking in the context of LBT. Thus, it is valuable to investigate how integrating an explicit state model checking algorithm might influence the performance of LBT. Model checkers explore the state space to verify conformance with user-defined correctness requirements, typically represented as Linear Temporal Logic (LTL) formulas. If a property violation is detected, it is presented as a counterexample. NuSMV and SAL employ different algorithms for generating and displaying counterexamples. This paper specifically examines the effect of SPIN-generated counterexamples on the LBT process. Evaluation metrics include Total LBT Iterations, First Bug Reporting Time (in milliseconds), Counterexample Length, Precision, and Efficiency, among others. Total Model Checking Time (in milliseconds) captures the cumulative time spent verifying the model over all iterations. SPIN consistently requires the least time for all specifications compared to other model checkers. As a result, experiments demonstrate that SPIN is more efficient when integrated with LBT, leading to faster convergence of the LBT hypothesis to the target System Under Test (SUT) in comparison to NuSMV and SAL
ML-Driven Lightweight Botnet Detection System for IoT-Networks
Get PDFThe integration of cloud computing with the Internet of Things (IoT) seeks to create seamless connections between humans and devices, enhancing applications in areas like smart healthcare and home automation. However, this also brings significant security challenges. Our study addresses the critical need for an efficient anomaly detection system specifically designed for IoT-enabled cloud computing environments, a gap not previously explored at this scale. Utilizing the IoT-23 dataset, we evaluated various feature selection techniques in conjunction with classification algorithms to develop a lightweight anomaly detection model. Our results demonstrate that the decision tree classifier, paired with the correlation coefficient method for feature selection, achieved an impressive 99.98% accuracy rate, with an average processing time of just 5.2 seconds. This combination proved to be the most effective for real-time anomaly detection, presenting a promising approach for ensuring robust security in IoT networks as connectivity continues to grow
Applying Agile Principles and Methods in Industries Outside IT Landscape: A Systematic Literature Review
Get PDFAgile methods have become increasingly popular in various industries because of many benefits over the common waterfall like methodologies. Whereas, there are still many issues and uncertainties faced by co-workers, or those who want to use a flexible approach within a controlled environment. These challenges need to be identified and need to be addressed. In this research, we conducted a systematic literature review to identify challenges perceived by practitioners during their working or adopting agile methodologies. We developed and followed a Systematic Literature Review (SLR) approach for searching agile related studies to extract different challenges. In this research, we divided the SLR into three categories, which was further divided into sub-categories. After searching the related articles using SLR, we present our findings on the challenges of implementing agile methodologies in various type of organizations. This study found the generic agile challenges that can faced by any organization by adopting agile principles. These generic challenges are related to some areas such as managerial, communication, product development, and cultural domains. These challenges can relate to both with-in and outside organizations. We discussed these challenges in detail. To conclude this research, we determined that agile has strong potential to be followed by any organization. This proposed study analyzed that agile has challenges of its modification for other organizations, but these challenges can be solved and agile can be used as effective technique by most of the companies
Image Compression Exploration using Discrete Wavelets Transform Families and Level
Get PDFThis analysis paper is based on Discrete Wavelets Transform (DWT) for image compression using wavelets families and levels. The DW transforms the image or data into frequency components that match its resolution scale while the compression removes duplication and unwanted information on the receiver side. Wavelets in compression observe the whole image very finely and thus produce no blocking artifacts. Thus, wavelets are high-quality image compression used in many real-world applications i.e. image, multimedia, biometric and biological analysis, computer graphics and image processing, etc. In this investigation, first of all, various compression methods have been compared. It is validated based on compression ratio that DWT is the optimal choice. Secondly, for experimental and analysis purposes, random real-time digital images both RGB and greyscale have been used as a dataset. The assessment images have been converted to grayscale if RGB, decomposed using wavelet levels, and compressed using wavelet families. Threshold coefficients have been evaluated by the Birge-Massart strategy using two scenarios i.e. simulator control thresholding and increasing threshold. Birge-Massart thresholding is best for the compression of still images in wavelet transform. e evaluation and comparison of various wavelet families and decomposition levels were conducted based on criteria such as image compression effectiveness, retained energy, and zero coefficients. The size of original, compressed, and decompressed images has also been computed and displayed for analysis purposes. The analysis of wavelet families and decomposition levels indicated that increasing levels up to a certain range for decomposition purposes in various wavelet compression families enhances image smoothness consistently. With image smoothness, roughness, and noise spikes in images have been reduced. However, it is observed that after specific levels, image quality degradation has been observed. The significance and novelty of the work provide analysis for appropriate and effective quality image compression using DWT families and levels in different applications. The purpose is to reduce need-based storage requirements and lightweight transmission. Additionally, the optimum compression algorithm in DWT families and levels is also found based on the results. As selection of wavelet filters and decomposition level play an important role in achieving an effective compression performance because no filter performs the best for all images