International Journal of Innovations in Science & Technology
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Hybrid Technique for Estimating Fetal Head Circumference Using Ultrasound Imaging
Get PDFIntroduction: Analysis of fetal head shape is crucial for assessing head growth and detecting abnormalities in fetuses. In traditional clinical practice, Head Circumference (HC) is determined by manually fitting an ellipse to the fetal skull based on 2D ultrasound images.
Novelty Statement: To address this, an automated method integrating image processing techniques with U-net variant have been developed to achieve maximum accuracy of fetal head circumference detection on HC18 dataset. This method aims to enhance precision in HC delineation, thereby improving clinical reliability.
Material and Method: This study proposed a method that combines image processing techniques (noise removal, edge detection, segmentation) with a Residual U-net model for detecting the boundary of the fetal skull using HC18 dataset.
Results and Discussion: The results of this method outperformed a simple residual u-net model in terms of accuracy. The proposed method is evaluating using the HC18 challenge dataset, achieving a Dice coefficient of 97.99%, a mean difference of 5.86 mm, and a mean Hausdorff distance of 0.56 mm compared to manual annotations. These results demonstrate the effectiveness of the proposed method in accurately delineating the fetal skull boundary.
Concluding remarks: Furthermore, the proposed method shows comparability with state-of-the-art techniques in the field
Predictive Maintenance Using Deep Learning: Enhancing Reliability and Reducing Electrical System Downtime
Get PDFPredictive Maintenance (PM) is crucial for enhancing the reliability of electrical systems and minimizing unscheduled outages. However, the conventional methodology lacks the ability to address the escalating and diverse problems of the modern complex environment. The current study provides an alternative approach to carrying out the predictive maintenance activity based on the use of deep learning models that enhance conventional procedures. For the given analysis, we used an artificial data set consisting of 10,000 samples and 14 variables, such as air temperature, process temperature, flipping rate, and tool wear level. We conducted a self-assessment using the specified models to confirm their effectiveness in predicting various failure modes and forms, such as tool wear and heat dissipation conking. This research demonstrated that deep learning models, specifically LSTMs, outperform the established statistical methods in predicting equipment failures. LSTMs provided high accuracy and predicted the failing system before it happened. Furthermore, integrating deep learning with the statistical method that is normally used for anomaly detection improves the model\u27s stability and reliability. The evaluation\u27s findings emphasize the potential of deep learning algorithms for expanding the range of PM applications to achieve better and faster failure predictions. The beneficial thing about this approach is that it presents a means for addressing the inherent problems of large electrical systems’ predictive maintenance that are beyond the scope of traditional practice
Site Suitability Analysis of Smog Cutting Towers in District Lahore, Pakistan
Get PDFIntroduction/Importance of Study:
Lahore, one of Pakistan’s most populous cities, is home to a diverse demographic comprising residents from various ethnic, linguistic, and cultural backgrounds. Recently, the city has been grappling with severe smog-related challenges, primarily due to vehicular emissions, industrial activities, and the burning of crop residues in neighboring agricultural regions. These factors have exacerbated numerous environmental and health issues, making the situation increasingly dire.
Novelty Statement:
Lahore’s 24 major hospitals, while indicative of a robust healthcare infrastructure, also underscore the vulnerability of a significant number of patients to the adverse effects of smog. The Normalized Difference Moisture Index (NDMI) reveals limited water availability in Lahore, which hinders natural cleansing mechanisms like rainfall that typically help remove pollutants from the atmosphere.
Material and Methods:
This research relies on secondary data sources, including Sentinel 2 imagery, to analyze urban expansion and moisture levels in the city. Traffic control data has been used to pinpoint areas with high traffic congestion, while elevated air pollution levels have been overlaid with hospital locations, marked using Google Earth data. This comprehensive approach aims to mitigate smog\u27s impact in Lahore by identifying areas most in need of smog-cutting towers.
Results and Discussion:
The research methodology focused on key parameters such as urban expansion, traffic choke points, vulnerable populations in hospitals, and the overall moisture index, which highlights the city’s capacity to retain moisture. The analysis identified major congestion areas, including Garhi Shahu, McLeod Road, Mughalpura, and Shahdara, as significant sources of pollution. The NDMI further confirmed limited water availability, exacerbating smog retention. To address these challenges, the strategic placement of four smog-cutting towers within a 5 km range in Lower Mall Road, Thokar Niaz Baig, Mughalpura, and Model Town has been recommended. These locations, characterized by high traffic congestion, elevated air pollution levels, and significant urban development, present a targeted approach to reducing smog in Lahore.
Concluding Remarks:
The identified locations in Lahore—Lower Mall Road, Thokar Niaz Baig, Mughalpura, and Model Town—are crucial for the effective mitigation of smog. The strategic placement of at least four smog-cutting towers within these areas will significantly influence the rate of smog production, retention, and impact, given the high traffic congestion, elevated pollution levels, and substantial urban development in these regions
Exploring the Spectrum Power Fractal Scaling Parameters by Hurst Range Increment - Second Order Moment Generation Techniques
Get PDFSpectral power analysis was employed to assess the Fractal Dimension (FD) and explore fractal scaling using Hurst increment ranges and second-order moment relations in the context of urban population trends. This research aimed to scrutinize population trends in Karachi over both uneven periods (1729 to 1946) and even periods (1951 to 2020) using non-parametric Mann-Kendall tests and Hurst error accuracy testing. The primary focus was on analyzing spectrum power fractal scaling through Hurst exponent ranges and second-order moment generation. The FD results indicated irregular (1.371) and regular (1.058) intervals within the inequality range of 1 < D < 1.5. The log-population trend cumulatively increased from 3.0 in 1729 to 5.72 in 1946, and from 6.05 in 1951 to 7.36 in 2020, suggesting that the fractal dimension is more appropriately fitted for total regular intervals. The second-order and range exponents were H2ndM (0.60 ± 0.09) and H-Range (0.83 ± 0.05) for the uneven period (1729 to 1946), and H2ndM (0.85 ± 0.06) and H-Range (0.93 ± 0.02) for the even period (1951 to 2020). The study\u27s results demonstrate that the range increment method is suitable and consistent across both long and short intervals. For regular intervals, the Hurst exponents show a linear relationship, indicating stability in the population trend analysis
Combatting Illegal Logging with AI-powered IoT Devices for Forest Monitoring
Get PDFThis research presents a comprehensive strategy for tackling illegal logging by leveraging Artificial Intelligence (AI) and Internet of Things (IoT) technologies. In high-risk forestry areas, sensors-equipped Internet of Things devices are used to continuously monitor and detect the sound of the surroundings. The AI component uses machine learning methods to identify potential unlawful logging activities by accurately detecting and distinguishing sound patterns associated with chainsaw and logging operations such as tree cutting and also detecting natural disasters like wildfires. When such activities are detected by these smart AI-powered IoT devices installed in the forest, real-time notifications are generated after such activity which allows surrounding enforcement agencies, such as the forest department, to intervene promptly. By providing a targeted and prompt solution to the issue of illicit logging, this strategy supports biodiversity preservation and sustainable forest management
Contrasting Impact of Start State on Performance of a Reinforcement Learning Recommender System
Get PDFA recommendation problem and RL problem are very similar, as both try to increase user satisfaction in a certain environment. Typical recommender systems mainly rely on history of the user to give future recommendations and doesn’t adapt well to current changing user demands. RL can be used to evolve with currently changing user demands by considering a reward function as feedback. In this paper, recommendation problem is modeled as an RL problem using a squared grid environment, with each grid cell representing a unique state generated by a biclustering algorithm Bibit. These biclusters are sorted according to their overlapping and then mapped to a squared grid. An RL agent then moves on this grid to obtain recommendations. However, the agent has to decide the most pertinent start state that can give best recommendations. To decide the start state of the agent, a contrasting impact of different start states on the performance of RL agent-based RSs is required. For this purpose, we applied seven different similarity measures to determine the start state of the RL agent. These similarity measures are diverse, attributed to the fact that some may not use rating values, some may only use rating values, or some may use global parameters like average rating value or standard deviation in rating values. Evaluation is performed on ML-100K and FilmTrust datasets under different environment settings. Results proved that careful selection of start state can greatly improve the performance of RL-based recommender systems
Urban Flooding and Climate Change Vulnerability-A Case Study of North Karachi
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rban flooding in Karachi has been exacerbated by insufficient climate resilience measures, inadequate urban planning, and underdeveloped drainage systems. These deficiencies have led to widespread flooding in residential and commercial areas, causing significant damage to infrastructure and amenities. North Karachi, a densely populated suburb, is particularly vulnerable due to its proximity to major water bodies such as the Lyari River (LR) and Gujjar Nala (GN). The area\u27s elevation ranges from 5 to 96 meters, creating a natural slope towards the southeast, making areas in this direction, including UCs 3, 5, 6, and 8, highly prone to flooding. This vulnerability is further influenced by the geographical layout, with the Lyari River and the surrounding Pub Ranges affecting rainfall runoff patterns. To evaluate flood vulnerability in North Karachi Town, various analyses were performed. Elevation data, sourced from Google Earth Pro, was converted from vector to raster format using ArcMap’s interpolation tool. This analysis revealed a slope from the northwest to the southeast, influenced by the Pub Ranges to the west and the Lyari River to the east. UCs 4, 7, and 9 are especially at risk due to their lower elevations and proximity to the Lyari River. The Normalized Difference Vegetation Index (NDWI) was employed to assess vegetation stress. Pre-monsoon NDWI values ranged from -0.26898 to -0.04352, indicating severe water stress. Post-monsoon values ranged from -0.2021 to 0.04597, with the maximum value of 0.04597 corresponding to humid and flooded conditions. The study highlights the crucial need to maintain clear waterways to manage flood risks effectively. Authorities should focus on ensuring that drains and river channels are free of debris and encroachments to mitigate future flooding
A Demographic Fuzzy Similarity Computation Method for Recommender Systems
Get PDF In this allegedly never-ending stream of e-commerce, it is crucial to offer high-quality suggestions so that consumers can choose wisely from a wide range of picks. Recommender Systems (RS) has proven to be an essential instrument for improving sales of online vendors and enabling consumers with personalized product recommendations. Collaborative Filtering (CF), an extensively preferred approach for Recommender Systems, provides suggestions based on the ratings of users with similar interests. The primary operating component in CF is to measure similarity among items or users. Recommender Systems use a user-item matrix, which is often highly sparse and suffers from cold start, ultimately leading to imprecise recommendations. Instead of relying merely on ratings that are uncertain and can be fake, we integrated the demographic information of users with CF to attain more precise predictions and recommendations in our work. To cope with the uncertainty factor in the recommendation process and to depict the physical world more realistically, we applied the Fuzzy set theory to users’ ratings and demographic features. ML-100K and ML-latest-small datasets are used to evaluate the accuracy of the proposed similarity measure. Compared to the most advanced methods, our proposed demographic fuzzy similarity computation method exhibits considerable achievements in terms of MAE, RMSE, and coverage metrics on standard recommendation datasets, which we used for experimentation
Pedagogical Suitability: A Software Metrics-Based Analysis of Java and Python
Get PDFProgramming is one of the foundational skills essential for computer science professionals, yet attaining proficiency in this skill is widely acknowledged as a formidable challenge. The intrinsic complexity of programming is often cited as the primary factor contributing to its difficulty. The choice of programming language for IP courses typically relies on past experiences and empirical evidence, rather than on a quantitative basis, which can affect its effectiveness and suitability for novice learners. The study presented in this article conducted a quantitative analysis of Java and Python to assess their suitability for use in IP courses. The analysis involved evaluating programs based on a total of 210 elementary programming algorithms using HCM. The results of the study indicated that Python programs, compared to Java programs, have a reduced reliance on lexical elements, are less complex, and have a smaller code size. Additionally, Python was found to produce less complex programs and required less effort and time for development and maintenance. Moreover, Python programs tend to have fewer bugs. Overall, the study concluded that Python is better suited for IP courses than Java. The novelty of this study lies in its quantitative comparison of Java and Python using HCM, revealing that Python is more appropriate for IP courses due to its lower complexity, reduced development effort, and fewer bugs
Computational Analysis of Model Houses of Da Kali KOR in Matta Swat
Get PDFNatural disasters such as floods and earthquakes, exacerbated by global warming and environmental degradation, pose significant challenges for modern architecture. This study critically evaluates a rural residential house in Sambat, Matta Swat, known locally as "da Kali KOR," focusing on its sustainability, climate responsiveness, contextual relevance, and use of indigenous materials and methods. It stands out for its in-depth analysis of a rural dwelling\u27s sustainability impact, a topic often overlooked in architectural research. Through detailed site surveys, climate analysis, and assessments of materials and structures, the study identifies issues like poor drainage control, inefficient orientation, wasteful space utilization, and user discomfort, which threaten environmental integrity and human well-being. By exploring the root causes of these problems, it proposes sustainable solutions for long-term improvement. By shedding light on these issues, the research contributes valuable insights to the discourse on sustainability in architecture and urban planning, offering guidance on enhancing rural residential design for better environmental and human outcomes