International Journal of Innovations in Science & Technology
Not a member yet
813 research outputs found
Sort by
Resilience Assessment of Urban Areas in Peshawar, Pakistan, in Response to Climate Change Impacts
Get PDFIntroduction of the Study: The Urban areas in Peshawar, Pakistan, are increasingly at risk from climate change impacts, necessitating a thorough resilience assessment to mitigate these risks and enhance adaptation strategies.
Novelty Statement: This study introduces a novel framework specifically tailored to Peshawar’s context, focusing on urban resilience—a topic that has not been extensively explored before.
Material and Method: A mixed-method approach was employed, including household surveys, focus group discussions, stakeholder interviews, and the analysis of secondary data from satellite imagery and government reports.
Result and Discussion: The major findings reveal significant vulnerabilities in infrastructure, diverse community perceptions of climate risks, and varying effectiveness of current adaptation measures. The capacity for emergency response demonstrated by local institutions underscores a critical need for capacity building. The study highlights both strengths and weaknesses in urban resilience, emphasizing the importance of institutional support and community engagement.
Concluding Remarks: The study provides targeted recommendations to enhance resilience-building efforts in Peshawar, aiming to improve the city’s ability to withstand and adapt to climate change impacts
The Evaluating Chlorine Dosage for Effective Disinfection and Antimicrobial Resistance Profiling in Drinking Water Under Climate Change Influences
No full textIntroduction/Importance of Study: Climate patterns, such as heavy rainfall and flooding, can introduce contaminants into water sources, leading to increased microbial loads. Chlorine disinfection is essential in mitigating these risks by effectively destroying pathogens.
Novelty Statement: This study investigates the effectiveness of different chlorine disinfectant dosages in eliminating disease-causing microorganisms and assessing antimicrobial resistance (AMR) in drinking water.
Material and Method: A biofilm annular reactor (BAR) setup was utilized to assess the impact of chlorination on pathogenic microorganisms. Three chlorine doses were tested: 0.5 mg/L, 1 mg/L, and 1.5 mg/L. Samples were collected and analyzed for AMR. Five selective bacterial strains were isolated using the membrane filtration method, and antibiotic sensitivity was evaluated using the standardized Kirby-Bauer disc diffusion test.
Result and Discussion: The study isolated five gram-negative bacteria on selective agar: E. coli, Salmonella, Shigella, Pseudomonas, and Vibrio cholerae. Their antimicrobial resistance to five antibiotics (amoxicillin, AML 5 µg; ampicillin, AMP 10 µg; Azithromycin, AZM 15 µg; ceftriaxone, CRO 30 µg; and imipenem, IPM 10 µg) was tested on Mueller-Hinton (MH) media. Azithromycin demonstrated the highest activity against all isolates. The optimal chlorine concentration for removing these bacteria from water was 1.5 mg/L, due to chlorine’s high reactivity.
Concluding Remarks: The study concludes that a chlorine concentration of 1.5 mg/L is optimal for pathogen removal from water, and Azithromycin exhibited exceptional effectiveness against all resistant gram-negative bacterial isolates
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
A Framework of Software as a Service Using a Crowdsourcing Approach: A Case Study of Smart Classroom
Get PDFIntroduction/Importance of Study: Crowdsourcing can be effectively utilized to identify factors and develop modules by creating a platform where individuals contribute their ideas and suggestions. This research investigates the application of crowdsourcing-based cloud resources managed on a global scale, bringing together diverse skills to handle workloads on cloud platforms. Despite inherent challenges such as quality control due to the varied locations of contractors, and communication issues including language barriers, differing time zones, and security concerns, crowdsourcing provides a robust framework. It enables software developers to access a vast talent pool and deliver services more quickly and efficiently.
Novelty Statement: The crowdsourcing framework leverages the collective wisdom of diverse individuals to solve problems and generate ideas. In a smart classroom setting, this approach can be applied by setting clear objectives, engaging students through appropriate platforms, fostering collaboration, collecting data via surveys or discussions, analyzing results, and using insights to enhance learning experiences. By leveraging students\u27 contributions, educators can enhance collaboration, creativity, and engagement in the classroom, ultimately enriching the learning process for all participants.
Materials and Methods: This research is divided into three phases:
Identification Phase: Challenges are identified through a systematic literature review (SLR).
Implementation Phase: Identified factors are shortlisted to design a framework.
Validation Phase: The framework is validated using a smart classroom case study.
Results and Discussion: Our findings indicate that smart classrooms provide an opportunity to investigate how students adopt technology and innovation. Survey results show that both teachers and students believe smart classrooms enhance their knowledge and perceived ease of use, demonstrating the benefits of this educational approach.
Concluding Remarks: By exploring the case of the smart classroom, this research challenges existing pedagogical methods and introduces innovative ways to engage students through new technology acceptance perspectives. This study highlights the potential of crowdsourcing in creating more effective and interactive learning environments
Monsoon 2022 Floods and Its Impacts on Agriculture Land Using Geospatial Approaches: A Case Study of Khyber Pakhtunkhwa Province Pakistan
Get PDFCountry of Pakistan is at higher risk because of climate change. The country affected by the extreme heat wave happened in May followed by devastating flood disaster in august 2022. Pakistan faced numerous disasters in near past. Pakistan faced a very high magnitude of earthquake round about 7.6 in between 2010 and 2014, in addition in 2022 we were the victims of severe floods across the country. Such events have an adverse effect on the financial figures of the country. Floods affect the whole province Khyber Pakhtunkhwa Pakistan but 09 districts were severely affected by the monsoon flood 2022. In southern districts DI khan (Dera Ismail khan) and Tank district were severely affected by the floods. In northern districts swat, Dir lower, Dir upper and some areas of District Chitral were affected. In the central Division District Nowshera, Charsadda and Peshawar were affected by the recent floods. Filed data were collected and bring them to Geospatial format for further analysis and then physically verified the damages of floods 2022. The results disclosed that in DI khan division, District DI khan and sub division DI khan recent monsoon spell damaged 1377.544215 Sq.km crop area, the cropped area damaged fully or partially in district tank and sub division tank is 270.146935 sq.km. In Peshawar division the crop land of District Charsadda were badly damaged where the sugarcane and maize crops were affected. Area calculated using GIS in district Charsadda that was damaged is 117.555732 Sq.km. In district Nowshera a total of 467.744999 and 30.081483 sq.km crop area were damaged in District Peshawar. The northern part of Khyber Pakhtunkhwa occupies hilly area and the residents relies on agriculture practices and gardening. In Malakand division district swat was much affected by the current spell of Monsoon where 122.38179 sq.km area of main crop and orchards were damaged. Similarly, 63.603461 sq.km area of District lower Dir, 15.147068 sq.km of upper Dir and 575.678 sq.km crop land of District Chitral were hit by the floods happened in 2022
Adapting Transfer Learning for Accurate ECG Based Heart Disease Classification
Get PDFECG signals are widely used for analyzing heart rhythms and detecting abnormalities. This study presents an experimental evaluation of a Deep CNN model for classifying ECG scalograms. Using publicly available datasets containing records from 242 patients, the study aims to classify three different cardiovascular diseases: Congestive Heart Failure (CHF), Myocardial Infarction (MI), and Coronary Artery Disease (CAD). The raw ECG signals undergo several preprocessing steps, including up-sampling, removal of noise and artifacts, and conversion into 2D images. Continuous Wavelet Transform (CWT) is applied to represent the ECG signals as 2D scalograms. The experiments in this work are conducted using a Deep CNN model and the pre-trained Inception V3 model, which achieved accuracies of 96.87% and 90.11%, respectively, on the CWT scalograms of the ECG datasets. The results were thoroughly analyzed, and the model’s performance was compared with other existing studies in the field
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
A Novel Guard Zone Based Multiple Access Protocol for Autonomous D2D Cellular Communication
Get PDFIn today\u27s interconnected and digitally driven era, Device-to-Device (D2D) communication has emerged as a transformative paradigm, enabling direct and efficient interactions between nearby devices with or without traditional network infrastructure. This research introduces a novel communication protocol that autonomously facilitates D2D communication in network-constrained environments, maximizing node engagement and ensuring reliable data transmission while addressing the inherent challenges faced by existing Medium Access Control (MAC) protocols, such as Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA). The study specifically focuses on developing a protocol that enables real-time information sharing among neighboring nodes without network assistance. To evaluate the protocol\u27s performance, comprehensive simulations were conducted using MATLAB, assessing its effectiveness in increasing the number of active nodes and reducing collision probability. The results demonstrate that the proposed protocol significantly decreases interference while enhancing data throughput and energy efficiency, achieving a 15% increase in active node pairs and a notable 4% reduction in collision probability. By minimizing contention overhead and optimizing data transmission, the protocol effectively lowers latency, ensuring reliable communication in environments lacking network support. Furthermore, it conserves energy by reducing idle listening, thereby extending battery life and promoting sustainable wireless communication systems. This research provides a robust solution to enhance D2D communication in isolated environments, paving the way for more resilient wireless ecosystems
Semantic Segmentation Based Lightweight Lane Detection Network (LW Net) for Intelligent Vehicles
Get PDFA novel lane detection system is proposed for intelligent vehicles. A key feature of this system is its lightweight design, which requires less computational power. Our lightweight network (LW Net) for semantic segmentation comprises convolutional and separable convolutional layers. We designed a total of six lightweight encoder models (LW Net-A, LW Net-B, LW Net-C, LW Net-D, LW Net-E, and LW Net-F), each paired with matching decoders. The first group of three models is based on depth D1, while the remaining models are based on depth D2. In these models, convolutional layers are either fully or partially replaced by separable convolutional layers. The lightweight network LW Net-A achieved an 88% reduction in training parameters, along with a 2.45% increase in test accuracy compared to the benchmark Seg Net model. Meanwhile, LW Net-F attained a 2% increase in test accuracy and a remarkable 94% reduction in training parameters compared to the benchmark Seg Net model. Overall, the proposed models are less computationally demanding than other benchmark networks, without compromising the pixel accuracy of the semantic model
Effects of Filters in Retinal Disease Detection on Optical Coherence Tomography (OCT) Images Using Machine Learning Classifiers
Get PDFOptical Coherence Tomography (OCT) is an essential, non-invasive imaging technique for producing high-resolution images of the retina, crucial in diagnosing and monitoring retinal conditions such as DME, CNV, and DRUSEN. Despite its importance, there is a pressing need to enhance the early detection and treatment of these common eye diseases. While deep learning methods have shown higher accuracy in classifying OCT images, the potential for machine learning approaches, particularly in terms of data size and computational efficiency, remains underexplored. This study generates models for detecting retinal disease on a publicly available dataset of retinal OCT images using machine learning classifiers with the help of image feature extractions. It classifies the given retinal OCT images as DME, CNV, DRUSEN, and NORMAL. Firstly, it extracts image features using appropriate methods and then it is trained, after training it passes through machine learning classifiers to classify the given input images, and then it is tested to get a better accuracy performance. The above steps are iterated by varying over the pre-processing techniques in which we first resize the image into 100 x 100 after resizing, we remove the noise by using Gaussian Blur and then normalize the image. We systematically benchmark its performance against established built-in methods, such as HOG, LBP, and FOSF. This comparative analysis serves to assess the efficacy of finding the best approach in relation to these widely recognized methods. The proposed experiments based on these approaches reveal that the use of HOG on this dataset outperforms with SVM classifier with a maximum accuracy of 78.8%