mirugwe alex
Alex is a self-motivated and result orientated Data Scientist with practical experience in delivering valuable insights via data analytics and advanced data-driven methods.
Skills:-
- Proficient in R and moderate in Python (for statistical analysis).
- Strong understanding of predictive modeling and Machine Learning Algorithms.
- Good understanding of data mining, cleaning, and modeling.
- Efficient in graphical modeling and data visualization.
- Proficient in SQL databases.
- Analytics of Social Media Data.
- An in-depth understanding of Deep Learning using Neural Networks.
Phone: +256701120524/+27737062534
Address: Plot 20A, Kawalya Kaggwa Close, Kololo Kampala Uganda
Skills:-
- Proficient in R and moderate in Python (for statistical analysis).
- Strong understanding of predictive modeling and Machine Learning Algorithms.
- Good understanding of data mining, cleaning, and modeling.
- Efficient in graphical modeling and data visualization.
- Proficient in SQL databases.
- Analytics of Social Media Data.
- An in-depth understanding of Deep Learning using Neural Networks.
Phone: +256701120524/+27737062534
Address: Plot 20A, Kawalya Kaggwa Close, Kololo Kampala Uganda
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Papers by mirugwe alex
costly, time-consuming and prone to errors, leading to inaccurate diagnoses. Therefore, it is essential to find more effective and efficient alternative methods for cervical cancer screening to improve access in LMICs and alleviate the burden of cervical cancer.
Objective: The purpose of this study is to develop an automated pre-cervical cancer screening algorithm to detect precancerous cervical lesions.
Materials and methods: We developed a cancer screening algorithm using a 21-layer deep-learning convolution neural network trained on a dataset of 2300 images collected from local sources and some obtained from Kaggle.
Results: The best-performing classifier had an Area Under Curve (AUC) of the accuracy of 91.37%, a precision of 88.80%, a recall of 94.69%, an F1 score of 91.65% and an AUC of 96.0%.
Conclusion: The development and implementation of automated pre-cervical cancer screening algorithms have the potential to revolutionize cervical cancer detection and contribute significantly to reducing the burden of the disease, particularly in resource-limited settings.
-Treman bird feeding garden at the Cornell Ornithology Laboratory in Ithaca, New York. At this station, Axis P11448-LE cameras are used to capture the recordings from feeders perched on the edge of both sapsucker woods and its 10-acre ponds. This site mainly attracts forest species like chickadees (Poecile atricapillus), red-winged blackbirds (Agelaius phoeniceus), and woodpeckers (Picidae). A total of 2065 images were captured from this location.
- Fort Davis in Western Texas, USA. At this site, a total of 30 hummingbird feeder cams are hosted at an elevation of over 5500 feet. From this site, 1440 images were captured.
-Sachatamia Lodge in Mindo, Ecuador. This site has a live hummingbird feed watcher that attracts over 132 species of hummingbirds including:
Fawn-breasted Brilliant, White-necked Jacobin, Purple-bibbed Whitetip, Violet-tailed Sylph, Velvet-purple Coronet, and many others. A total of 2063 images were captured from this location.
-Morris County, New Jersey, USA. Feeders at this location attract over 39 species including Red-bellied Woodpecker, Red-winged Blackbird, Purple Finch, Blue Jay, Pine Siskin, Hairy Woodpecker, and others. Footage at this site is captured by an Axis P1448-LE Camera and Axis T8351 Microphone. A total of 1876 images were recorded from this site.
-Canopy Lodge in El Valle de Anton, Panama. Over 158 bird species visit this location annually and these include Gray-headed Chachalaca, Ruddy Ground-Dove, White-tipped Dove, Green Hermit, and others. A total of 1600 images were captured.
-Southeast tip of South Island, New Zealand. At this site, nearly 10000 seabirds visit this location annually and a total of 1548 images were captured.
The Cornell Lab of Ornithology is an institute dedicated to biodiversity conversation with the main focus on birds through research, citizen science, and education. The autoscreen software was used to capture the images from the live feeds and images of approximately 1 Megapixel (Joint Photographic Experts Group) JPEG coloured images of resolution $1366\times 768 \times 3$ pixels were collected (https://sourceforge.net/projects/autoscreen/). The software was taking a new image every 30 seconds and were captured during different times of the day in order to avoid a sample biased dataset. In total, 10592 images were collected for this study.
These models were trained and then after evaluated against validation and test sets and using confusion matrices to obtain classification and misclassification rates. The logistic model was trained using glmnet R package, Tree package for classification tree model, randomForest for both Bagging and Random Forest Models, and gbm package for Gradient Boosted Model.
The best accuracy was obtained from the Random Forest Model with a classification rate of 93.21% when it was evaluated against the test set. Light sensor is also the most significant variable in predicting whether the office room is occupied or not, this was observed in all the five models.
can constantly collect, analyze, evaluate and validate our environment to get more control of the factors
that influence it. With over a decade of intensive research and development, wireless sensor network
technology has been emerging as viable solution to many innovative applications. Various audio wireless
consumer devices have been developed over years. But these wireless TV headphones use Bluetooth
technology which comes with a number of drawbacks; high power consumption, high cost, short distance
coverage and limited number of users at time. In this project, we have developed a wireless TV audio
transceiver (transmitter to multiple receivers) using Arduino and nRF24L01 module. The nRF24L01
transceiver module uses the 2.4 GHz band and it can operate with band rates from 250 kbps up to 2 Mbps.
If used in closed space and with lower band rate its range can reach up to 100 meters.
The Wireless audio system operates at Radio Frequency (RF) signals. Specifically, it utilizes
IEEE802.15.4 standard to transmit the audio signals. The system is designed to transmit and receive the
audio signal about 2.4Ghz frequencies. The system is powered using a 9Vdc battery. The Wireless audio
system utilizes IEEE802.15.4 Radio Frequency (RF) standard to transmit the audio signals. IEEE
standard 802.15.4 offers the fundamental lower network layers of a Wireless Personal Area Network
(WPAN) and focuses on low-cost, low-power communication between devices. The system will be
designed to transmit and receive the audio signal using 2.4Ghz band.
The transmitter converts the input analog signal from the TV audio socket to digital signal using the
microcontroller. The digital signal will then be sent to the nRF24L01 module which modulates it using
Gaussian Frequency Shift Keying (GFSK) modulation scheme and transmits it at 2.4GHz.
The receivers use GFSK modulation to demodulate the digital signal received and convert it to an analog
signal using the microcontroller. The analog signal is amplified by LM386 circuit where users can
individually modulate the volume of sound of their preferences. LM386 is a low voltage audio
amplifier and frequently used in battery powered music devices.
Drafts by mirugwe alex
Objective: The purpose of this study is to develop an automated pre-cervical cancer screening algorithm to detect precancerous cervical lesions.
Methodology: We developed a cancer screening algorithm using a 21-layer deep-learning convolution neural network trained on a dataset of 2300 images collected from local sources and some obtained from Kaggle.
Results: The best-performing classifier had an AUC of the accuracy of 91.37%, a precision of 88.80%, a recall of 94.69%, an F1 score of 91.65%, and an AUC of 96.0%.
Conclusion: The development and implementation of automated pre-cervical cancer screening algorithms have the potential to revolutionize cervical cancer detection and contribute significantly to reducing the burden of the disease, particularly in resource-limited settings.
The dataset of 200 observations and 7 variables was divided into training and testing sets in a ratio of 8:2 respectively. The model was fitted using the lm() function of R on the train set and tested on the testing set using predict() function. And the model fitness was deeply analyzed to understand how well it fits the data.
Using Lasso regularization approach, the model was improved and this helped to identify the most important predictors in estimating the amount of tip received by the waiter. And also an interaction of size and smoker was included in the final model which greatly improved its data fitness.
Even beginners will find this manual easy to use because its a self-teaching guide.
Conference Presentations by mirugwe alex
costly, time-consuming and prone to errors, leading to inaccurate diagnoses. Therefore, it is essential to find more effective and efficient alternative methods for cervical cancer screening to improve access in LMICs and alleviate the burden of cervical cancer.
Objective: The purpose of this study is to develop an automated pre-cervical cancer screening algorithm to detect precancerous cervical lesions.
Materials and methods: We developed a cancer screening algorithm using a 21-layer deep-learning convolution neural network trained on a dataset of 2300 images collected from local sources and some obtained from Kaggle.
Results: The best-performing classifier had an Area Under Curve (AUC) of the accuracy of 91.37%, a precision of 88.80%, a recall of 94.69%, an F1 score of 91.65% and an AUC of 96.0%.
Conclusion: The development and implementation of automated pre-cervical cancer screening algorithms have the potential to revolutionize cervical cancer detection and contribute significantly to reducing the burden of the disease, particularly in resource-limited settings.
-Treman bird feeding garden at the Cornell Ornithology Laboratory in Ithaca, New York. At this station, Axis P11448-LE cameras are used to capture the recordings from feeders perched on the edge of both sapsucker woods and its 10-acre ponds. This site mainly attracts forest species like chickadees (Poecile atricapillus), red-winged blackbirds (Agelaius phoeniceus), and woodpeckers (Picidae). A total of 2065 images were captured from this location.
- Fort Davis in Western Texas, USA. At this site, a total of 30 hummingbird feeder cams are hosted at an elevation of over 5500 feet. From this site, 1440 images were captured.
-Sachatamia Lodge in Mindo, Ecuador. This site has a live hummingbird feed watcher that attracts over 132 species of hummingbirds including:
Fawn-breasted Brilliant, White-necked Jacobin, Purple-bibbed Whitetip, Violet-tailed Sylph, Velvet-purple Coronet, and many others. A total of 2063 images were captured from this location.
-Morris County, New Jersey, USA. Feeders at this location attract over 39 species including Red-bellied Woodpecker, Red-winged Blackbird, Purple Finch, Blue Jay, Pine Siskin, Hairy Woodpecker, and others. Footage at this site is captured by an Axis P1448-LE Camera and Axis T8351 Microphone. A total of 1876 images were recorded from this site.
-Canopy Lodge in El Valle de Anton, Panama. Over 158 bird species visit this location annually and these include Gray-headed Chachalaca, Ruddy Ground-Dove, White-tipped Dove, Green Hermit, and others. A total of 1600 images were captured.
-Southeast tip of South Island, New Zealand. At this site, nearly 10000 seabirds visit this location annually and a total of 1548 images were captured.
The Cornell Lab of Ornithology is an institute dedicated to biodiversity conversation with the main focus on birds through research, citizen science, and education. The autoscreen software was used to capture the images from the live feeds and images of approximately 1 Megapixel (Joint Photographic Experts Group) JPEG coloured images of resolution $1366\times 768 \times 3$ pixels were collected (https://sourceforge.net/projects/autoscreen/). The software was taking a new image every 30 seconds and were captured during different times of the day in order to avoid a sample biased dataset. In total, 10592 images were collected for this study.
These models were trained and then after evaluated against validation and test sets and using confusion matrices to obtain classification and misclassification rates. The logistic model was trained using glmnet R package, Tree package for classification tree model, randomForest for both Bagging and Random Forest Models, and gbm package for Gradient Boosted Model.
The best accuracy was obtained from the Random Forest Model with a classification rate of 93.21% when it was evaluated against the test set. Light sensor is also the most significant variable in predicting whether the office room is occupied or not, this was observed in all the five models.
can constantly collect, analyze, evaluate and validate our environment to get more control of the factors
that influence it. With over a decade of intensive research and development, wireless sensor network
technology has been emerging as viable solution to many innovative applications. Various audio wireless
consumer devices have been developed over years. But these wireless TV headphones use Bluetooth
technology which comes with a number of drawbacks; high power consumption, high cost, short distance
coverage and limited number of users at time. In this project, we have developed a wireless TV audio
transceiver (transmitter to multiple receivers) using Arduino and nRF24L01 module. The nRF24L01
transceiver module uses the 2.4 GHz band and it can operate with band rates from 250 kbps up to 2 Mbps.
If used in closed space and with lower band rate its range can reach up to 100 meters.
The Wireless audio system operates at Radio Frequency (RF) signals. Specifically, it utilizes
IEEE802.15.4 standard to transmit the audio signals. The system is designed to transmit and receive the
audio signal about 2.4Ghz frequencies. The system is powered using a 9Vdc battery. The Wireless audio
system utilizes IEEE802.15.4 Radio Frequency (RF) standard to transmit the audio signals. IEEE
standard 802.15.4 offers the fundamental lower network layers of a Wireless Personal Area Network
(WPAN) and focuses on low-cost, low-power communication between devices. The system will be
designed to transmit and receive the audio signal using 2.4Ghz band.
The transmitter converts the input analog signal from the TV audio socket to digital signal using the
microcontroller. The digital signal will then be sent to the nRF24L01 module which modulates it using
Gaussian Frequency Shift Keying (GFSK) modulation scheme and transmits it at 2.4GHz.
The receivers use GFSK modulation to demodulate the digital signal received and convert it to an analog
signal using the microcontroller. The analog signal is amplified by LM386 circuit where users can
individually modulate the volume of sound of their preferences. LM386 is a low voltage audio
amplifier and frequently used in battery powered music devices.
Objective: The purpose of this study is to develop an automated pre-cervical cancer screening algorithm to detect precancerous cervical lesions.
Methodology: We developed a cancer screening algorithm using a 21-layer deep-learning convolution neural network trained on a dataset of 2300 images collected from local sources and some obtained from Kaggle.
Results: The best-performing classifier had an AUC of the accuracy of 91.37%, a precision of 88.80%, a recall of 94.69%, an F1 score of 91.65%, and an AUC of 96.0%.
Conclusion: The development and implementation of automated pre-cervical cancer screening algorithms have the potential to revolutionize cervical cancer detection and contribute significantly to reducing the burden of the disease, particularly in resource-limited settings.
The dataset of 200 observations and 7 variables was divided into training and testing sets in a ratio of 8:2 respectively. The model was fitted using the lm() function of R on the train set and tested on the testing set using predict() function. And the model fitness was deeply analyzed to understand how well it fits the data.
Using Lasso regularization approach, the model was improved and this helped to identify the most important predictors in estimating the amount of tip received by the waiter. And also an interaction of size and smoker was included in the final model which greatly improved its data fitness.
Even beginners will find this manual easy to use because its a self-teaching guide.