International Journal of Innovations in Science & Technology
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Optimized Coverage and Capacity Planning of Wi-Fi Network based on Radio Frequency Modeling & Propagation Simulation
Get PDFInvestigation for optimized coverage and capacity planning of Wi-Fi network is carried out in the testbed for the purpose of optimization in terms of Received Signal Strength Indicator (RSSI), Signal to Noise & Interference Ratio (SNIR), Interference + Noise (I+N), downlink/uplink data rate and user capacity. The plan is carried out by conducting a site prediction survey through Altair’s Win Prop Software which is a Radio Frequency (RF) modeling and signal propagation simulation software, using the configuration of actual Wireless Local Area Network-Access Points (WLAN-APs). First, the map of the testbed with all respective material properties is drawn in Win Prop’s Wall Manager (Wall Man) Tool as a 3-Dimentional (3D) model. Then that 3D model is implemented in Win Prop’s Propagation Manager (ProMan) Tool where APs are deployed and wave propagation analysis as well as capacity planning is done. Results are analyzed for optimal signal strength, data rate, and user handling capacity. The results are validated by a smartphone-embedded software known as Cellular-Z. The average optimization increase in coverage, downlink & uplink data rates is 3.95 dB, 2.53 Mbps & 3.42 Mbps respectively
AI-Driven Prediction of Electricity Production and Consumption in Micro-Hydropower Plant
Get PDFMicro hydropower plants must effectively manage demand response to preserve operational firmness and prevent system breakdowns. This research focuses on accomplishing a fine balance while predicting consumption and production, which is significant for upholding system integrity. The study delves into predictive modeling methods to forecast patterns in the production and consumption of electricity over an array of time horizons. We adopted a custom sliding window mechanism, in which actual and predicted values are used to predict the next hour of electricity. We set a baseline to resolve this and examined various algorithms, focusing on RNN-LSTM and CGP-LSTM. The CGP-LSTM forecasting output sequences with different time horizons precisely outperform the RNN-LSTM. The dataset utilized is downloaded from the Kaggle website. 50% of the data is used to train the models, and the rest is used to test the models. This work deals with the complex fluctuations in the demand response system and provides electricity production and consumption predictions. CGP-LSTM model gave a training MAPE of 6.67 (Accuracy of 93.33%) and a testing MAPE of 6.68 (accuracy of 93.32%) for the next three hours; on the other hand, LSTM gave a training MAPE of 6.53 (accuracy of 93.47%) and testing MAPE of 7.46 (accuracy of 92.54%) for the next three hours. The results offer a base for further developments and improvements in the field, drawing attention to more effective and reliable energy management capabilities in micro hydropower plants.
CGP-LSTM model gave a training MAPE of 6.67 (Accuracy of 93.33%) and a testing MAPE of 6.68 (accuracy of 93.32%) for the next three hours; on the other hand, LSTM gave a training MAPE of 6.53 (accuracy of 93.47%) and testing MAPE of 7.46 (accuracy of 92.54%) for the next three hours.
The results offer a base for further developments and improvements in the field, drawing attention to more effective and reliable energy management capabilities in micro hydropower plants
Drinking Water Monitoring: Computer Vision Kit for Early E.coli Detection
Get PDFThis work presents an easy-to-use and accurate method to find up to 1 coliform unit (CFU) of a pathogenic bacterium i.e., Escherichia coli (E. coli) in 100ml of drinking water in 6-8 Hours of the incubation period. A larger number of CFUs is easy to detect and incubation time is reduced to 5-7 Hours for the testing samples containing more than 20 CFUs. Normally in laboratories up to 1 ml of a water sample is spread on an endo agar medium and incubated for about 24 Hours, and the E. coli coliform in metallic green color becomes visible through the naked eye. Which has a limitation of finding 1 CFU in just 1 ml of water and a limitation of a large amount of time. In the proposed work Membrane filtration method is used for experiments and a microscopic camera with deep learning algorithms i.e., yolov5 and yolov8 is used for the early detection and counting of E. coli colonies. This system is generalized on the field data of 8k images taken from different cities\u27 water samples in Pakistan. Yolov5s model achieved a mean average precession ([email protected]) of .949, while the latest release version yolov8 achieved [email protected] of 0.950. An automatic imagery system is developed that takes the images just by placing a petri dish in it processes those images through Raspberry Pi, and shows the detected colonies on the screen, while remote users can use a low-cost microscopic camera manually with a developed mobile application
Deep Learning Based Identification and Categorization of Various Phases of Diabetic Retinopathy
Get PDFDiabetic Retinopathy is a growing disease that affects the human retina of diabetic patients and if it is left untreated it leads to loss of vision. Early diagnosis and accurate classification of DR stages are important for immediate intervention and efficient control. Therefore, this study focuses on the classification of different stages of diabetic retinopathy in retinal images by using a DL (deep learning) model named Densenet121. The dataset used in this research contains various collections of color fundus images obtained from diabetic patients, labelled with corresponding disease stages. The dataset used was taken from Kaggle named APTOS 2019. Standard metrics such as accuracy, recall, F1-score, and precision are used to measure the effectiveness of the proposed model. The proposed DL based classification model shows encouraging results and has achieved a high level of accuracy across various severity levels. This model offers an automated method for detection and classification of the disease facilitating early diagnosis. Overall, this study advances automated diagnosis to lessen the burden of diabetic retinopathy
Heterotrophic Denitrification Of Wastewater By Using Melanoidin As Sole Carbon Source
Get PDFRemoving nitrate effects through heterotrophic denitrification is an effective approach to treating water or wastewater containing these contaminants. The effective management of wastewater is of paramount importance to mitigate its adverse impacts on both human health and the environment. This study delves into the biodegradation and removal of nitrate and nitrite by introducing melanoidin as a carbon source, the research explores the influence of batch test strategy using a heterotrophic denitrification process for removal of toxicity by using inoculum from MCR (master culture reactor) for 48 hours in which four controls (C1, C2, C3, C4) and five test sample of different dilutions of melanoidin i.e. 100 ppm, 250 ppm, 500ppm, 700 ppm and 1000 ppm were used. The results demonstrate that remarkable removal of nitrate, nitrite, and TOC was found in 100 ppm, 250 ppm and 500 ppm dilutions, with a marked reduction of Total organic carbon (TOC), indicating successful toxicity removal, 750 ppm and 1000 ppm dilutions were not affected by denitrification in TOC removal. Subsequent denitrification of the T1, T2, and T3 samples showcases the potent synergy between treatment processes. Through the use of a C:N ratio of 2:1 and 3:1, 98.14% of nitrate was successfully removed within 48 hours. High-Performance Ion Chromatography (HPIC) was employed to analyze the samples treated with denitrification, revealing the complete elimination of toxicity. These results highlight the crucial role of melanoidin as a carbon source in denitrification, enabling thorough nitrate degradation and detoxification. This study underscores the importance of adopting innovative treatment methods to address the growing challenges associated with wastewater management
Monitoring Snow-Covered Dynamics and Impact on Climatic Change
Get PDFGlaciated areas play a crucial role in cooling the planet; however, their accelerated melting initiates a feedback loop that decreases Earth\u27s albedo, leading to further warming and increased melting. This phenomenon poses significant risks to Pakistan\u27s agricultural productivity and energy supply, particularly in the Himalayan, Karakoram, and Hindukush (HKH) mountain ranges. These regions are undergoing substantial changes due to global warming and regional climate variability. Glaciers and snow packs in these areas function as natural reservoirs, releasing vital meltwater during the summer to sustain river flows, especially the Indus River, which is essential for Pakistan\u27s agriculture, drinking water, and hydropower. This study aims to monitor the extent, mass, and distribution of snow cover in the HKH ranges to assess local vulnerability and provide a comprehensive evaluation of ongoing climate change impacts. By analyzing Landsat 5, 7, and 8’s Tier 1 Top of Atmosphere (TOA) reflectance products, the annual median snow cover from 1991 to 2020 was calculated to visualize and quantify snow cover dynamics in Hunza Nagar, Gilgit-Baltistan, Pakistan. The results revealed no significant trends in total snow cover, with only minor fluctuations and variations from the mean value, and notable reductions during strong El Niño years. These findings underscore the critical importance of glaciated areas and the threats posed by their melting. Ongoing monitoring and comprehensive regional assessments are vital to understanding the impacts of climate change on snow cover dynamics. Such efforts are essential for developing adaptive strategies to mitigate adverse effects on Pakistan\u27s water resources, agriculture, and energy systems
Evaluating the Effectiveness of Phase Difference in Early Drought Detection
Get PDFIntroduction. This research work focuses on how various phase relationships can enhance our understanding of the effects of drought on moisture deficiency in desert ecosystems, an extensive and damaging environmental phenomenon that affects natural ecosystems, economies, health, agriculture, and society.
Novelty Statement. The primary objective of this research is to inspect the lag time variance between fixed and dynamic lag windows correlated with NDVI, aiming to devise an optimal methodology for drought analysis in this region.
Material and Methods. Leveraging remote sensing data, this study delves into the complex drought dynamics of the Thar Desert, employing a comprehensive analysis of 22 years of CHIRPS rainfall time series data and MODIS NDVI (Normalized Difference Vegetation Index) product. This study performed a cross-correlation of rainfall and NDVI, comparing the lag time difference between fixed lag windows (16, 32, 48, 64 days) and dynamic lag windows (ranging from 4 to 64 days with incremental steps) against 22 years of NDVI data of MODIS.
Results and Discussions. The preliminary results showed that dynamic lag windows of 4, 8, 12, 16, …, and 64 days exhibit the highest correlation with NDVI, with a lag time of 40 days showing maximum correlation. These findings suggest that dynamic lag windows capture the temporal variability of drought impact on vegetation more effectively compared to fixed lag windows in the Thar Desert. The same work was done with a sub-dynamic lag window ranging in between the highly correlated lag episodes of dynamic and fix windows respectively i.e.,40 days and 48 days, concluding that a lag phase of 42 days exhibits the highest correlation with vegetation more effectively. Furthermore, the study unveils a significant drought event in 2002, showcasing the sensitivity of the dynamic lag approach in detecting extreme drought occurrences.
Concluding Remarks. This research not only advances drought analysis methodologies in arid regions but also underscores the imperative for future investigations to explore the generalizability of dynamic lag windows across diverse regions and evaluate their predictive capacity in forecasting drought-induced vegetation changes
Clinical Prediction of Female Infertility Through Advanced Machine Learning Techniques
Get PDFInfertility in females implies failure by such women to conceive even after having at least one year of intercourse without using any contraceptives. Infertility can be caused by a variety of factors, including ovulation problems, blocked fallopian tubes, hormone imbalances, and abnormalities of the uterus and so on. Infertility can negatively impact people\u27s emotional, psychological, and social well-being. Our proposed study utilizes advanced machine learning techniques to present an innovative and novel method for predicting female infertility. We analyzed a dataset with medical attributes related to reproductive health using logistic regression, Naive Bayes, Support Vector Machines (SVM), and Random Forest algorithms. The Random Forest algorithm achieved an outstanding accuracy rate of 93%, with its exceptional capabilities. The findings show that in the future, this model can be used to diagnose infertility early and provide personalized treatment recommendations. The results of this study have practical implications for reproductive healthcare, as well as providing much-needed support to infertile couples and individuals
Enhanced Brain Tumor Diagnosis with EfficientNetB6: Leveraging Transfer Learning and Edge Detection Techniques
Get PDFCorrect identification of brain tumors is crucial for determining the subsequent steps in patient management and prognosis. This study introduces a novel approach by mimicking three enhanced deep learning models EfficientNetB0, EfficientNetB6, and ResNet50 on a dataset of 7022 MRI instances, each depicting one of four varieties of brain tumors. The research was conducted using advanced neural network architectures, leveraging transfer learning to improve model performance. Results indicated that EfficientNetB6 achieved the highest testing accuracy at 99.39%, outperforming EfficientNetB0 and ResNet50, which recorded test accuracies of 95% and 97% respectively. Evaluation metrics further highlighted the superior performance of EfficientNetB6, with a precision, recall, and F1 score all at 99%. These findings demonstrate the significant potential of deep learning algorithms in enhancing the diagnostic accuracy of brain tumors, suggesting their implementation in clinical settings could lead to better diagnosis and treatment options
Analyzing the Impacts of Soapstone Dust on Respiratory System of Mine Workers Through Structural Equation Modelling Technique: A Case Study of Sherwan Soapstone Mines, Abbottabad, Pakistan
Get PDFDust produced in mining has a substantial impact on worker’s health resulting in severe respiratory diseases. Researchers mainly focused on the dust problems faced in surface mining whereas the dust produced in underground soapstone mines has received comparatively less attention. This study evaluates self-reported respiratory symptoms and medical examinations of underground mine workers in soapstone mines. It establishes a relationship between the respiratory illness factors and its symptoms, providing new insight into the analysis. Demographic and other respiratory symptoms-related data is collected through questionnaires from underground soapstone mine workers, located in the Abbottabad area, with medical data from 60 of these workers obtained through medical examinations. The collected data is subsequently analyzed using Structural Equation Modelling and regression analysis to investigate the relationship between the evaluated factors in the dust analysis. The dust assessment shows that it is primarily composed of silica, with small particle sizes that are smaller than the threshold limit value and pose a risk of silicosis. The questionnaire data indicates that about 75% of workers exhibit symptoms of respiratory diseases, the majority of them are laborers and old age workers whereas the medical examinations revealed that 80% of workers are affected by lung infections. The Structural Equation Modelling demonstrates that dust inhalation has a stronger effect on symptom occurrence (β = 0.485, p < 0.001) compared to dust severity (β = 0.207, p < 0.05). These results are concerning and underscore the need for interventions, and the adoption of adequate respiratory protection measures for safeguarding the health of workers