This document describes a proposed system to detect haemoglobin levels non-invasively using deep learning techniques. The system would use a deep convolutional neural network (DCNN) trained on images of participants' conjunctiva taken with a smartphone camera. The DCNN would be trained to predict numeric haemoglobin values by comparing them to validated complete blood count (CBC) reports. The goal is to develop an accurate, non-invasive method for real-time haemoglobin detection to help diagnose anaemia and other conditions. The proposed system aims to explore how well a DCNN can detect haemoglobin levels compared to existing non-invasive techniques.
Real-time and Non-Invasive Detection of Haemoglobin level using CNNIRJET Journal
This document describes a study that aims to detect haemoglobin levels in a non-invasive and real-time manner using convolutional neural networks (CNNs). The researchers collected a dataset of finger images with varying haemoglobin levels from blood donation camps. They trained a CNN model on this dataset to classify haemoglobin levels based on image features. The CNN was tested on additional finger images and able to detect haemoglobin levels in real-time without drawing blood, providing advantages over traditional invasive methods like faster results and no patient discomfort or biohazards. The proposed non-invasive method using deep learning could help diagnose blood-related conditions earlier.
This document summarizes a student project presentation on developing a blood group detection system using image processing. The project is being conducted by 4 students in their 7th semester of Computer Science engineering under the guidance of a professor. The presentation includes an abstract, objectives, introduction on blood groups and their importance, scope of the project, literature survey of existing methods, problem definition, proposed work including the system requirements and references. The proposed work is to use a non-invasive approach involving image sensors and spectroscopic data to determine blood groups quickly and accurately in emergencies.
This document summarizes a study that used proteomics to identify serum biomarkers for Alzheimer's disease (AD) and mild cognitive impairment (MCI) by analyzing serum samples from patients selected based on their PiB-PET imaging scores. The researchers used isobaric tagging and liquid chromatography-tandem mass spectrometry to perform proteome profiling on serum from control, MCI, and AD patients. They identified 79 and 72 differentially expressed proteins in MCI and AD serum, respectively, compared to controls. Integrated analysis with brain tissue data identified three biomarker candidates related to proteolysis: PCSK9, F13A1, and DCD. Validation in independent serum samples confirmed elevated levels of these candidates in MCI
IRJET- Automated Blood Group Recognition System using Image ProcessingIRJET Journal
This document proposes an automated blood group recognition system using image processing. The system aims to develop an embedded system to perform blood tests based on ABO and Rh blood typing systems using image processing algorithms. This would help reduce human intervention and allow complete testing autonomously from adding antigens to generating results. The system aims to develop accurate results in the shortest time possible while storing results for future reference. The system determines blood type by detecting agglutination in blood samples mixed with antigens through image processing techniques like local binary pattern, morphological operations, and HSL luminance, eliminating risks of human errors in traditional methods.
Anemia is a state of poor health where there is presence of low amount of red blood cell in blood stream.
This research aims to design a model for prediction of Anemia in children under 5 years of age using
Complete Blood Count reports. Data are collected from Kanti Children Hospital which consist of 700 data
records. Then they are preprocessed, normalized, balanced and selected machine learning algorithms were
applied. It is followed by verification, validation along with result analysis. Random Forest is the best
performer which showed accuracy of 98.4%. Finally, Feature Selection as well as Ensemble Learning
methods, Voting, Stacking, Bagging and Boosting were applied to improve the performance of algorithms.
Selecting the best performer algorithm, stacking with other algorithms, bagging it, boosting it are very
much crucial to improve accuracy despite of any time issue for prediction of anemia in children below 5
years of age.
Anemia is a state of poor health where there is presence of low amount of red blood cell in blood stream.
This research aims to design a model for prediction of Anemia in children under 5 years of age using
Complete Blood Count reports. Data are collected from Kanti Children Hospital which consist of 700 data
records. Then they are preprocessed, normalized, balanced and selected machine learning algorithms were
applied. It is followed by verification, validation along with result analysis. Random Forest is the best
performer which showed accuracy of 98.4%. Finally,
This study compared the effectiveness of same-day light-emitting diode fluorescence microscopy (LED-FM) to the conventional method for diagnosing tuberculosis (TB) in Chhattisgarh, India. Of over 1700 presumptive TB patients who provided all three sputum samples, 218 (13%) were smear-positive. The same-day method identified 200 cases (11.7%) while the conventional method identified 217 cases (12.7%). The same-day method missed 18 cases (8.3%) that were identified by the conventional method. While LED-FM is more sensitive, these findings suggest that some smear-positive cases may be missed using the same-day diagnosis method compared to the conventional
Impact of Big Data & Artificial Intelligence in Drug Discovery & Development ...Nick Brown
Oral Presentation given at European Drug Discovery Innovation & Outsourcing Programme on 12th September 2023 in Barcelona. Overview around impact for AstraZeneca R&D from examples in the past 5+ years, including machine learning for safety assessment, augmenting digital pathology for image quantification & segmentation, and examples applying AI for right dose - identifying risk factors for CV patients and automated Population PK model prediction.
Real-time and Non-Invasive Detection of Haemoglobin level using CNNIRJET Journal
This document describes a study that aims to detect haemoglobin levels in a non-invasive and real-time manner using convolutional neural networks (CNNs). The researchers collected a dataset of finger images with varying haemoglobin levels from blood donation camps. They trained a CNN model on this dataset to classify haemoglobin levels based on image features. The CNN was tested on additional finger images and able to detect haemoglobin levels in real-time without drawing blood, providing advantages over traditional invasive methods like faster results and no patient discomfort or biohazards. The proposed non-invasive method using deep learning could help diagnose blood-related conditions earlier.
This document summarizes a student project presentation on developing a blood group detection system using image processing. The project is being conducted by 4 students in their 7th semester of Computer Science engineering under the guidance of a professor. The presentation includes an abstract, objectives, introduction on blood groups and their importance, scope of the project, literature survey of existing methods, problem definition, proposed work including the system requirements and references. The proposed work is to use a non-invasive approach involving image sensors and spectroscopic data to determine blood groups quickly and accurately in emergencies.
This document summarizes a study that used proteomics to identify serum biomarkers for Alzheimer's disease (AD) and mild cognitive impairment (MCI) by analyzing serum samples from patients selected based on their PiB-PET imaging scores. The researchers used isobaric tagging and liquid chromatography-tandem mass spectrometry to perform proteome profiling on serum from control, MCI, and AD patients. They identified 79 and 72 differentially expressed proteins in MCI and AD serum, respectively, compared to controls. Integrated analysis with brain tissue data identified three biomarker candidates related to proteolysis: PCSK9, F13A1, and DCD. Validation in independent serum samples confirmed elevated levels of these candidates in MCI
IRJET- Automated Blood Group Recognition System using Image ProcessingIRJET Journal
This document proposes an automated blood group recognition system using image processing. The system aims to develop an embedded system to perform blood tests based on ABO and Rh blood typing systems using image processing algorithms. This would help reduce human intervention and allow complete testing autonomously from adding antigens to generating results. The system aims to develop accurate results in the shortest time possible while storing results for future reference. The system determines blood type by detecting agglutination in blood samples mixed with antigens through image processing techniques like local binary pattern, morphological operations, and HSL luminance, eliminating risks of human errors in traditional methods.
Anemia is a state of poor health where there is presence of low amount of red blood cell in blood stream.
This research aims to design a model for prediction of Anemia in children under 5 years of age using
Complete Blood Count reports. Data are collected from Kanti Children Hospital which consist of 700 data
records. Then they are preprocessed, normalized, balanced and selected machine learning algorithms were
applied. It is followed by verification, validation along with result analysis. Random Forest is the best
performer which showed accuracy of 98.4%. Finally, Feature Selection as well as Ensemble Learning
methods, Voting, Stacking, Bagging and Boosting were applied to improve the performance of algorithms.
Selecting the best performer algorithm, stacking with other algorithms, bagging it, boosting it are very
much crucial to improve accuracy despite of any time issue for prediction of anemia in children below 5
years of age.
Anemia is a state of poor health where there is presence of low amount of red blood cell in blood stream.
This research aims to design a model for prediction of Anemia in children under 5 years of age using
Complete Blood Count reports. Data are collected from Kanti Children Hospital which consist of 700 data
records. Then they are preprocessed, normalized, balanced and selected machine learning algorithms were
applied. It is followed by verification, validation along with result analysis. Random Forest is the best
performer which showed accuracy of 98.4%. Finally,
This study compared the effectiveness of same-day light-emitting diode fluorescence microscopy (LED-FM) to the conventional method for diagnosing tuberculosis (TB) in Chhattisgarh, India. Of over 1700 presumptive TB patients who provided all three sputum samples, 218 (13%) were smear-positive. The same-day method identified 200 cases (11.7%) while the conventional method identified 217 cases (12.7%). The same-day method missed 18 cases (8.3%) that were identified by the conventional method. While LED-FM is more sensitive, these findings suggest that some smear-positive cases may be missed using the same-day diagnosis method compared to the conventional
Impact of Big Data & Artificial Intelligence in Drug Discovery & Development ...Nick Brown
Oral Presentation given at European Drug Discovery Innovation & Outsourcing Programme on 12th September 2023 in Barcelona. Overview around impact for AstraZeneca R&D from examples in the past 5+ years, including machine learning for safety assessment, augmenting digital pathology for image quantification & segmentation, and examples applying AI for right dose - identifying risk factors for CV patients and automated Population PK model prediction.
Non invasive anemia screening- Mobile appsSaiLakshmi128
This document discusses non-invasive methods for screening anemia, specifically smartphone apps that use images of fingernails. It provides background on anemia and current invasive screening methods. It then describes several non-invasive methods studied, including pulse oximetry, occlusion spectroscopy, and photoplethysmography. The document focuses on smartphone apps that use algorithms to analyze fingernail images and measure hemoglobin levels without a blood draw. Studies found good agreement between hemoglobin measurements from app analyses of images and evaluations by hematologists. Non-invasive screening via smartphone app offers an easy, economic method for anemia screening.
Aims: Dried Blood Spot (DBS) sampling is a frequently used method to obtain Haemoglobin A1C (HbA1c) in clinical studies of freeliving populations. Under controlled conditions, DBS sampling is a valid and robust alternative to traditional Whole Blood (WB) sampling. The objective of this analysis was to investigate the impact of storage conditions on the validity of HbA1c assessed from DBS collected in free-living and to develop a method to correct for this type of error.
International Journal of Computational Engineering Research(IJCER) ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Flow cytometry is a technique that uses lasers and fluorescence to count and examine microscopic particles like cells. It can measure multiple parameters of individual cells as they flow in a liquid stream past the laser beam at thousands of cells per second. Components include a flow cell to arrange cells in a stream, optical systems to generate light signals, detectors to convert light signals to electrical signals, and read-out devices to analyze the results. Flow cytometry is used widely in clinical laboratories for applications like immunophenotyping, DNA analysis for malignancy, detecting enzymatic deficiencies, genetic diseases, and hematology analysis.
BLOOD TUMOR PREDICTION USING DATA MINING TECHNIQUEShiij
Healthcare systems generate a huge data collected from medical tests. Data mining is the computing
process of discovering patterns in large data sets such as medical examinations. Blood diseases are not an
exception; there are many test data can be collected from their patients. In this paper, we applied data
mining techniques to discover the relations between blood test characteristics and blood tumor in order to
predict the disease in an early stage, which can be used to enhance the curing ability. We conducted
experiments in our blood test dataset using three different data mining techniques which are association
rules, rule induction and deep learning. The goal of our experiments is to generate models that can
distinguish patients with normal blood disease from patients who have blood tumor. We evaluated our
results using different metrics applied on real data collected from Gaza European hospital in Palestine.
The final results showed that association rules could give us the relationship between blood test
characteristics and blood tumor. Also, it demonstrated that deep learning classifiers has the best ability to
predict tumor types of blood diseases with an accuracy of 79.45%. Also, rule induction gave us an
explanation of rules that describes both tumor in blood and normal hematology.
BLOOD TUMOR PREDICTION USING DATA MINING TECHNIQUEShiij
Healthcare systems generate a huge data collected from medical tests. Data mining is the computing
process of discovering patterns in large data sets such as medical examinations. Blood diseases are not an
exception; there are many test data can be collected from their patients. In this paper, we applied data
mining techniques to discover the relations between blood test characteristics and blood tumor in order to
predict the disease in an early stage, which can be used to enhance the curing ability. We conducted
experiments in our blood test dataset using three different data mining techniques which are association
rules, rule induction and deep learning. The goal of our experiments is to generate models that can
distinguish patients with normal blood disease from patients who have blood tumor. We evaluated our
results using different metrics applied on real data collected from Gaza European hospital in Palestine.
The final results showed that association rules could give us the relationship between blood test
characteristics and blood tumor. Also, it demonstrated that deep learning classifiers has the best ability to
predict tumor types of blood diseases with an accuracy of 79.45%. Also, rule induction gave us an
explanation of rules that describes both tumor in blood and normal hematology.
BLOOD TUMOR PREDICTION USING DATA MINING TECHNIQUEShiij
Healthcare systems generate a huge data collected from medical tests. Data mining is the computing process of discovering patterns in large data sets such as medical examinations. Blood diseases are not an exception; there are many test data can be collected from their patients. In this paper, we applied data mining techniques to discover the relations between blood test characteristics and blood tumor in order to predict the disease in an early stage, which can be used to enhance the curing ability. We conducted experiments in our blood test dataset using three different data mining techniques which are association rules, rule induction and deep learning. The goal of our experiments is to generate models that can distinguish patients with normal blood disease from patients who have blood tumor. We evaluated our
results using different metrics applied on real data collected from Gaza European hospital in Palestine. The final results showed that association rules could give us the relationship between blood test characteristics and blood tumor. Also, it demonstrated that deep learning classifiers has the best ability to predict tumor types of blood diseases with an accuracy of 79.45%. Also, rule induction gave us an explanation of rules that describes both tumor in blood and normal hematology.
This document presents a fuzzy logic approach for detecting anemia using clinical test results. It describes developing a fuzzy expert system with 3 input variables (hemoglobin, mean corpuscular volume, mean corpuscular hemoglobin concentration) and 1 output variable (type of anemia). Fuzzy sets and rules are defined to classify anemia based on the input clinical values. The system was tested on sample input values and correctly classified the type of anemia based on the fuzzy logic rules. The approach aims to help doctors more accurately detect anemia using a fuzzy expert system compared to probabilistic logic or relying solely on symptoms.
Medical Science is considered as a field of uncertainty, vagueness and complexity. Fuzzy logic plays an important role to deal with these uncertainty, vagueness and complexity. Detection of diseases in medical is a very difficult task. To improve accuracy rate engineers helping in detection of the diseases by developing the Expert System using Fuzzy Logic. Fuzzy logic consists of many valued logic. It has varying values in the range of 0 and 1 instead of fix values. In this study, we developed a Fuzzy Expert system to detect Anemia on the basis of Symptoms as well as clinical test.
EXPERIMENTAL IMPLEMENTATION OF EMBARRASINGLY PARALLEL PROCESS IN ANALYSIS OF ...ijesajournal
This document describes an experimental implementation of an embarrassingly parallel process to analyze blood glucose concentration using ATmega32 microcontrollers. The system was designed to handle multiple blood samples simultaneously using 4 sensor nodes connected to a master node via I2C bus. The sensor nodes operate in parallel to measure glucose levels, with the master node coordinating distribution of samples and collection of results. Evaluation showed the system achieved linear speedup in processing blood samples compared to serial methods.
Pitfalls in Performing and Interpreting IPSS 2021.pdffrancisco551255
This document discusses pitfalls in performing and interpreting inferior petrosal sinus sampling (IPSS) based on a literature review and case examples. Key points include:
1) IPSS cannot confirm ACTH-dependent Cushing syndrome - biochemical testing is required first to establish the diagnosis.
2) Successful catheter placement relies on operator experience, and anatomical variations can complicate interpretation.
3) In ambiguous cases, adjunctive tests like prolactin measurement and prolactin-adjusted ACTH ratios may provide additional information.
4) A stepwise approach considering all clinical and biochemical data is needed for accurate IPSS interpretation.
Blood Transfusion success rate prediction using Artificial IntelligenceIRJET Journal
This document discusses using machine learning models to predict whether patients will require an intraoperative blood transfusion during mitral valve surgery. Specifically, it examines using the XGBoost and gradient boost techniques to predict transfusion success rates. It finds that XGBoost achieves an accuracy of about 93% for predicting transfusions, compared to 90% for gradient boost, making XGBoost the better performing model. The document concludes that machine learning can successfully predict transfusion needs with an accuracy of 93% using XGBoost.
Detection of Parkinson’s disease (PD) at an early stage
is necessary for its treatment. The commonly used methods
available in the literature use observation of certain symptoms
such as Tremor, Loss of Smell and Troubled Sleeping, Moving or
Walking. The motion pattern in this disease can be characterized
by a spatio-temporal phenomenon that signifies gait recognition as
reported in the literature. However, non-invasive methods such as
use of Gait image sequences are handy in terms of cost and
comfort. In this paper we propose a statistical approach for
detection of Parkinson’s diseases by considering segmental feature
of gait image sequences by using Hidden Markov Model (HMM).
A set of key features from the image frames is identified during
the gait cycle. The input binary silhouette images are preprocessed
by morphological operations to fill the holes and remove noise. An
image feature vector is created from the outer contour of the
image sequences. From the feature vectors of the gait cycle, a set
of initial exemplars is constructed. The similarity between the
feature vector and the exemplar is measured by the inner product
distance. An HMM is trained iteratively using the Viterbi
algorithm and Baum-Welch algorithm and then used for detection
of Parkisonian gait. The characteristics of one dimensional HMM
best fit to one dimensional image vector thus the proposed method
reduces image feature from the two-dimensional plane to a onedimensional
vector. The statistical nature of the HMM makes it
robust to PD gait representation and recognition. The proposed
HMM-based method in LabVIEW and MATLAB is evaluated
using the CMU MoBo database as well as our own prepared
database for PD detection
Integrated hemolysis monitoring for bottom-up protein bioanalysisAnne Kleinnijenhuis
Triskelion developed an integrated LC-MS method to simultaneously quantify therapeutic proteins and quantify hemolysis in biological samples. The method uses tryptic peptides from hemoglobin as markers for hemolysis that are analyzed by LC-MS/MS alongside the target protein. This allows for objective hemolysis quantification without extra sample volume. The method was tested on cynomolgus monkey serum samples, showing UV-VIS and LC-MS results correlated better than visual hemolysis estimates. The concept can be applied to other sample types and parameters by selecting appropriate markers.
This document discusses different types of biosensors and their applications. It summarizes an experiment that used a glucometer, blood glucose assay, and pregnancy test to study biosensor concepts. The blood glucose assay and glucometer provided different results for blood glucose levels, with the assay being more accurate due to using serum rather than whole blood. The pregnancy test correctly identified a positive sample via the presence of hCG but has limitations as a qualitative test. Biosensors offer advantages like speed and ease of use but also have limitations in accuracy compared to conventional methods.
A FEASIBILITY STUDY OF REMOTE MONITORING OF CAPD PATIENT’S BLOOD PRESSURE AND
BLOOD GLUCOSE MEASUREMENTS VIA THE INTERNET. G. Pylypchuk, P. Jacobson, C. McAllister
University of Saskatchewan, St. Paul’s Hospital, Saskatoon, Saskatchewan. Regina, Saskatchewan
The purpose of this study was to determine the feasibility of remotely monitoring blood pressure (BP) and
glucose measurements in a cohort of diabetic patients receiving continuous ambulatory peritoneal
dialysis (CAPD).
Development and Validation of a Nomogram for Predicting Response to Neoadjuva...semualkaira
Retrospective analysis of clinical data on female patients with breast cancer was performed. Model 1 was developed by entering variables from the univariate analysis (P < 0.1) into a multivariate logistic regression analysis. Model 2 was developed via the stepwise forward-backward variable selection technique in partial least squares regression. For model 3, the least absolute shrinkage and selection operator (LASSO) method was used to choose suitable variables, followed by the multivariate logistic regression analysis.
Development and Validation of a Nomogram for Predicting Response to Neoadjuva...semualkaira
Retrospective analysis of clinical data on female
patients with breast cancer was performed. Model 1 was developed by entering variables from the univariate analysis (P < 0.1)
into a multivariate logistic regression analysis. Model 2 was developed via the stepwise forward-backward variable selection technique in partial least squares regression. For model 3, the least
absolute shrinkage and selection operator (LASSO) method was
used to choose suitable variables, followed by the multivariate
logistic regression analysis. Harrell’s C-index, calibration curves,
and decision curve analyses (DCA) were used to compare the
performance of the models. In the validation cohort, these results
were validated
Computer Aided Diagnosis for Screening the Shape and Size of Leukocyte Cell N...IJECEIAES
This document summarizes a study that developed a computer-aided diagnosis system to identify the shape and size of leukocyte cell nuclei from microscopic images. The system uses digital image processing techniques including template matching, thresholding, and morphological operations to extract nuclei from blood cell images. It then calculates the diameter of extracted nuclei both using Matlab functions and manually via Pythagorean theorem. Comparing the results found a small percentage of error, validating the automatic approach. The goal is to support low-cost, easy-to-use leukemia screening in remote areas by automating blood cell analysis traditionally done manually by specialists.
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
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Non invasive anemia screening- Mobile appsSaiLakshmi128
This document discusses non-invasive methods for screening anemia, specifically smartphone apps that use images of fingernails. It provides background on anemia and current invasive screening methods. It then describes several non-invasive methods studied, including pulse oximetry, occlusion spectroscopy, and photoplethysmography. The document focuses on smartphone apps that use algorithms to analyze fingernail images and measure hemoglobin levels without a blood draw. Studies found good agreement between hemoglobin measurements from app analyses of images and evaluations by hematologists. Non-invasive screening via smartphone app offers an easy, economic method for anemia screening.
Aims: Dried Blood Spot (DBS) sampling is a frequently used method to obtain Haemoglobin A1C (HbA1c) in clinical studies of freeliving populations. Under controlled conditions, DBS sampling is a valid and robust alternative to traditional Whole Blood (WB) sampling. The objective of this analysis was to investigate the impact of storage conditions on the validity of HbA1c assessed from DBS collected in free-living and to develop a method to correct for this type of error.
International Journal of Computational Engineering Research(IJCER) ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Flow cytometry is a technique that uses lasers and fluorescence to count and examine microscopic particles like cells. It can measure multiple parameters of individual cells as they flow in a liquid stream past the laser beam at thousands of cells per second. Components include a flow cell to arrange cells in a stream, optical systems to generate light signals, detectors to convert light signals to electrical signals, and read-out devices to analyze the results. Flow cytometry is used widely in clinical laboratories for applications like immunophenotyping, DNA analysis for malignancy, detecting enzymatic deficiencies, genetic diseases, and hematology analysis.
BLOOD TUMOR PREDICTION USING DATA MINING TECHNIQUEShiij
Healthcare systems generate a huge data collected from medical tests. Data mining is the computing
process of discovering patterns in large data sets such as medical examinations. Blood diseases are not an
exception; there are many test data can be collected from their patients. In this paper, we applied data
mining techniques to discover the relations between blood test characteristics and blood tumor in order to
predict the disease in an early stage, which can be used to enhance the curing ability. We conducted
experiments in our blood test dataset using three different data mining techniques which are association
rules, rule induction and deep learning. The goal of our experiments is to generate models that can
distinguish patients with normal blood disease from patients who have blood tumor. We evaluated our
results using different metrics applied on real data collected from Gaza European hospital in Palestine.
The final results showed that association rules could give us the relationship between blood test
characteristics and blood tumor. Also, it demonstrated that deep learning classifiers has the best ability to
predict tumor types of blood diseases with an accuracy of 79.45%. Also, rule induction gave us an
explanation of rules that describes both tumor in blood and normal hematology.
BLOOD TUMOR PREDICTION USING DATA MINING TECHNIQUEShiij
Healthcare systems generate a huge data collected from medical tests. Data mining is the computing
process of discovering patterns in large data sets such as medical examinations. Blood diseases are not an
exception; there are many test data can be collected from their patients. In this paper, we applied data
mining techniques to discover the relations between blood test characteristics and blood tumor in order to
predict the disease in an early stage, which can be used to enhance the curing ability. We conducted
experiments in our blood test dataset using three different data mining techniques which are association
rules, rule induction and deep learning. The goal of our experiments is to generate models that can
distinguish patients with normal blood disease from patients who have blood tumor. We evaluated our
results using different metrics applied on real data collected from Gaza European hospital in Palestine.
The final results showed that association rules could give us the relationship between blood test
characteristics and blood tumor. Also, it demonstrated that deep learning classifiers has the best ability to
predict tumor types of blood diseases with an accuracy of 79.45%. Also, rule induction gave us an
explanation of rules that describes both tumor in blood and normal hematology.
BLOOD TUMOR PREDICTION USING DATA MINING TECHNIQUEShiij
Healthcare systems generate a huge data collected from medical tests. Data mining is the computing process of discovering patterns in large data sets such as medical examinations. Blood diseases are not an exception; there are many test data can be collected from their patients. In this paper, we applied data mining techniques to discover the relations between blood test characteristics and blood tumor in order to predict the disease in an early stage, which can be used to enhance the curing ability. We conducted experiments in our blood test dataset using three different data mining techniques which are association rules, rule induction and deep learning. The goal of our experiments is to generate models that can distinguish patients with normal blood disease from patients who have blood tumor. We evaluated our
results using different metrics applied on real data collected from Gaza European hospital in Palestine. The final results showed that association rules could give us the relationship between blood test characteristics and blood tumor. Also, it demonstrated that deep learning classifiers has the best ability to predict tumor types of blood diseases with an accuracy of 79.45%. Also, rule induction gave us an explanation of rules that describes both tumor in blood and normal hematology.
This document presents a fuzzy logic approach for detecting anemia using clinical test results. It describes developing a fuzzy expert system with 3 input variables (hemoglobin, mean corpuscular volume, mean corpuscular hemoglobin concentration) and 1 output variable (type of anemia). Fuzzy sets and rules are defined to classify anemia based on the input clinical values. The system was tested on sample input values and correctly classified the type of anemia based on the fuzzy logic rules. The approach aims to help doctors more accurately detect anemia using a fuzzy expert system compared to probabilistic logic or relying solely on symptoms.
Medical Science is considered as a field of uncertainty, vagueness and complexity. Fuzzy logic plays an important role to deal with these uncertainty, vagueness and complexity. Detection of diseases in medical is a very difficult task. To improve accuracy rate engineers helping in detection of the diseases by developing the Expert System using Fuzzy Logic. Fuzzy logic consists of many valued logic. It has varying values in the range of 0 and 1 instead of fix values. In this study, we developed a Fuzzy Expert system to detect Anemia on the basis of Symptoms as well as clinical test.
EXPERIMENTAL IMPLEMENTATION OF EMBARRASINGLY PARALLEL PROCESS IN ANALYSIS OF ...ijesajournal
This document describes an experimental implementation of an embarrassingly parallel process to analyze blood glucose concentration using ATmega32 microcontrollers. The system was designed to handle multiple blood samples simultaneously using 4 sensor nodes connected to a master node via I2C bus. The sensor nodes operate in parallel to measure glucose levels, with the master node coordinating distribution of samples and collection of results. Evaluation showed the system achieved linear speedup in processing blood samples compared to serial methods.
Pitfalls in Performing and Interpreting IPSS 2021.pdffrancisco551255
This document discusses pitfalls in performing and interpreting inferior petrosal sinus sampling (IPSS) based on a literature review and case examples. Key points include:
1) IPSS cannot confirm ACTH-dependent Cushing syndrome - biochemical testing is required first to establish the diagnosis.
2) Successful catheter placement relies on operator experience, and anatomical variations can complicate interpretation.
3) In ambiguous cases, adjunctive tests like prolactin measurement and prolactin-adjusted ACTH ratios may provide additional information.
4) A stepwise approach considering all clinical and biochemical data is needed for accurate IPSS interpretation.
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This document discusses using machine learning models to predict whether patients will require an intraoperative blood transfusion during mitral valve surgery. Specifically, it examines using the XGBoost and gradient boost techniques to predict transfusion success rates. It finds that XGBoost achieves an accuracy of about 93% for predicting transfusions, compared to 90% for gradient boost, making XGBoost the better performing model. The document concludes that machine learning can successfully predict transfusion needs with an accuracy of 93% using XGBoost.
Detection of Parkinson’s disease (PD) at an early stage
is necessary for its treatment. The commonly used methods
available in the literature use observation of certain symptoms
such as Tremor, Loss of Smell and Troubled Sleeping, Moving or
Walking. The motion pattern in this disease can be characterized
by a spatio-temporal phenomenon that signifies gait recognition as
reported in the literature. However, non-invasive methods such as
use of Gait image sequences are handy in terms of cost and
comfort. In this paper we propose a statistical approach for
detection of Parkinson’s diseases by considering segmental feature
of gait image sequences by using Hidden Markov Model (HMM).
A set of key features from the image frames is identified during
the gait cycle. The input binary silhouette images are preprocessed
by morphological operations to fill the holes and remove noise. An
image feature vector is created from the outer contour of the
image sequences. From the feature vectors of the gait cycle, a set
of initial exemplars is constructed. The similarity between the
feature vector and the exemplar is measured by the inner product
distance. An HMM is trained iteratively using the Viterbi
algorithm and Baum-Welch algorithm and then used for detection
of Parkisonian gait. The characteristics of one dimensional HMM
best fit to one dimensional image vector thus the proposed method
reduces image feature from the two-dimensional plane to a onedimensional
vector. The statistical nature of the HMM makes it
robust to PD gait representation and recognition. The proposed
HMM-based method in LabVIEW and MATLAB is evaluated
using the CMU MoBo database as well as our own prepared
database for PD detection
Integrated hemolysis monitoring for bottom-up protein bioanalysisAnne Kleinnijenhuis
Triskelion developed an integrated LC-MS method to simultaneously quantify therapeutic proteins and quantify hemolysis in biological samples. The method uses tryptic peptides from hemoglobin as markers for hemolysis that are analyzed by LC-MS/MS alongside the target protein. This allows for objective hemolysis quantification without extra sample volume. The method was tested on cynomolgus monkey serum samples, showing UV-VIS and LC-MS results correlated better than visual hemolysis estimates. The concept can be applied to other sample types and parameters by selecting appropriate markers.
This document discusses different types of biosensors and their applications. It summarizes an experiment that used a glucometer, blood glucose assay, and pregnancy test to study biosensor concepts. The blood glucose assay and glucometer provided different results for blood glucose levels, with the assay being more accurate due to using serum rather than whole blood. The pregnancy test correctly identified a positive sample via the presence of hCG but has limitations as a qualitative test. Biosensors offer advantages like speed and ease of use but also have limitations in accuracy compared to conventional methods.
A FEASIBILITY STUDY OF REMOTE MONITORING OF CAPD PATIENT’S BLOOD PRESSURE AND
BLOOD GLUCOSE MEASUREMENTS VIA THE INTERNET. G. Pylypchuk, P. Jacobson, C. McAllister
University of Saskatchewan, St. Paul’s Hospital, Saskatoon, Saskatchewan. Regina, Saskatchewan
The purpose of this study was to determine the feasibility of remotely monitoring blood pressure (BP) and
glucose measurements in a cohort of diabetic patients receiving continuous ambulatory peritoneal
dialysis (CAPD).
Development and Validation of a Nomogram for Predicting Response to Neoadjuva...semualkaira
Retrospective analysis of clinical data on female patients with breast cancer was performed. Model 1 was developed by entering variables from the univariate analysis (P < 0.1) into a multivariate logistic regression analysis. Model 2 was developed via the stepwise forward-backward variable selection technique in partial least squares regression. For model 3, the least absolute shrinkage and selection operator (LASSO) method was used to choose suitable variables, followed by the multivariate logistic regression analysis.
Development and Validation of a Nomogram for Predicting Response to Neoadjuva...semualkaira
Retrospective analysis of clinical data on female
patients with breast cancer was performed. Model 1 was developed by entering variables from the univariate analysis (P < 0.1)
into a multivariate logistic regression analysis. Model 2 was developed via the stepwise forward-backward variable selection technique in partial least squares regression. For model 3, the least
absolute shrinkage and selection operator (LASSO) method was
used to choose suitable variables, followed by the multivariate
logistic regression analysis. Harrell’s C-index, calibration curves,
and decision curve analyses (DCA) were used to compare the
performance of the models. In the validation cohort, these results
were validated
Computer Aided Diagnosis for Screening the Shape and Size of Leukocyte Cell N...IJECEIAES
This document summarizes a study that developed a computer-aided diagnosis system to identify the shape and size of leukocyte cell nuclei from microscopic images. The system uses digital image processing techniques including template matching, thresholding, and morphological operations to extract nuclei from blood cell images. It then calculates the diameter of extracted nuclei both using Matlab functions and manually via Pythagorean theorem. Comparing the results found a small percentage of error, validating the automatic approach. The goal is to support low-cost, easy-to-use leukemia screening in remote areas by automating blood cell analysis traditionally done manually by specialists.
Similar to Bloodless Haemoglobin level Detection using Deep Convolution Neural Network (20)
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...IRJET Journal
This study compares the use of Stark Steel and TMT Steel as reinforcement materials in a two-way reinforced concrete slab. Mechanical testing is conducted to determine the tensile strength, yield strength, and other properties of each material. A two-way slab design adhering to codes and standards is executed with both materials. The performance is analyzed in terms of deflection, stability under loads, and displacement. Cost analyses accounting for material, durability, maintenance, and life cycle costs are also conducted. The findings provide insights into the economic and structural implications of each material for reinforcement selection and recommendations on the most suitable material based on the analysis.
Effect of Camber and Angles of Attack on Airfoil CharacteristicsIRJET Journal
This document discusses a study analyzing the effect of camber, position of camber, and angle of attack on the aerodynamic characteristics of airfoils. Sixteen modified asymmetric NACA airfoils were analyzed using computational fluid dynamics (CFD) by varying the camber, camber position, and angle of attack. The results showed the relationship between these parameters and the lift coefficient, drag coefficient, and lift to drag ratio. This provides insight into how changes in airfoil geometry impact aerodynamic performance.
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...IRJET Journal
This document reviews the progress and challenges of aluminum-based metal matrix composites (MMCs), focusing on their fabrication processes and applications. It discusses how various aluminum MMCs have been developed using reinforcements like borides, carbides, oxides, and nitrides to improve mechanical and wear properties. These composites have gained prominence for their lightweight, high-strength and corrosion resistance properties. The document also examines recent advancements in fabrication techniques for aluminum MMCs and their growing applications in industries such as aerospace and automotive. However, it notes that challenges remain around issues like improper mixing of reinforcements and reducing reinforcement agglomeration.
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...IRJET Journal
This document discusses research on using graph neural networks (GNNs) for dynamic optimization of public transportation networks in real-time. GNNs represent transit networks as graphs with nodes as stops and edges as connections. The GNN model aims to optimize networks using real-time data on vehicle locations, arrival times, and passenger loads. This helps increase mobility, decrease traffic, and improve efficiency. The system continuously trains and infers to adapt to changing transit conditions, providing decision support tools. While research has focused on performance, more work is needed on security, socio-economic impacts, contextual generalization of models, continuous learning approaches, and effective real-time visualization.
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...IRJET Journal
This document summarizes a research project that aims to compare the structural performance of conventional slab and grid slab systems in multi-story buildings using ETABS software. The study will analyze both symmetric and asymmetric building models under various loading conditions. Parameters like deflections, moments, shears, and stresses will be examined to evaluate the structural effectiveness of each slab type. The results will provide insights into the comparative behavior of conventional and grid slabs to help engineers and architects select appropriate slab systems based on building layouts and design requirements.
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...IRJET Journal
This document summarizes and reviews a research paper on the seismic response of reinforced concrete (RC) structures with plan and vertical irregularities, with and without infill walls. It discusses how infill walls can improve or reduce the seismic performance of RC buildings, depending on factors like wall layout, height distribution, connection to the frame, and relative stiffness of walls and frames. The reviewed research paper analyzes the behavior of infill walls, effects of vertical irregularities, and seismic performance of high-rise structures under linear static and dynamic analysis. It studies response characteristics like story drift, deflection and shear. The document also provides literature on similar research investigating the effects of infill walls, soft stories, plan irregularities, and different
This document provides a review of machine learning techniques used in Advanced Driver Assistance Systems (ADAS). It begins with an abstract that summarizes key applications of machine learning in ADAS, including object detection, recognition, and decision-making. The introduction discusses the integration of machine learning in ADAS and how it is transforming vehicle safety. The literature review then examines several research papers on topics like lightweight deep learning models for object detection and lane detection models using image processing. It concludes by discussing challenges and opportunities in the field, such as improving algorithm robustness and adaptability.
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...IRJET Journal
The document analyzes temperature and precipitation trends in Asosa District, Benishangul Gumuz Region, Ethiopia from 1993 to 2022 based on data from the local meteorological station. The results show:
1) The average maximum and minimum annual temperatures have generally decreased over time, with maximum temperatures decreasing by a factor of -0.0341 and minimum by -0.0152.
2) Mann-Kendall tests found the decreasing temperature trends to be statistically significant for annual maximum temperatures but not for annual minimum temperatures.
3) Annual precipitation in Asosa District showed a statistically significant increasing trend.
The conclusions recommend development planners account for rising summer precipitation and declining temperatures in
P.E.B. Framed Structure Design and Analysis Using STAAD ProIRJET Journal
This document discusses the design and analysis of pre-engineered building (PEB) framed structures using STAAD Pro software. It provides an overview of PEBs, including that they are designed off-site with building trusses and beams produced in a factory. STAAD Pro is identified as a key tool for modeling, analyzing, and designing PEBs to ensure their performance and safety under various load scenarios. The document outlines modeling structural parts in STAAD Pro, evaluating structural reactions, assigning loads, and following international design codes and standards. In summary, STAAD Pro is used to design and analyze PEB framed structures to ensure safety and code compliance.
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...IRJET Journal
This document provides a review of research on innovative fiber integration methods for reinforcing concrete structures. It discusses studies that have explored using carbon fiber reinforced polymer (CFRP) composites with recycled plastic aggregates to develop more sustainable strengthening techniques. It also examines using ultra-high performance fiber reinforced concrete to improve shear strength in beams. Additional topics covered include the dynamic responses of FRP-strengthened beams under static and impact loads, and the performance of preloaded CFRP-strengthened fiber reinforced concrete beams. The review highlights the potential of fiber composites to enable more sustainable and resilient construction practices.
Survey Paper on Cloud-Based Secured Healthcare SystemIRJET Journal
This document summarizes a survey on securing patient healthcare data in cloud-based systems. It discusses using technologies like facial recognition, smart cards, and cloud computing combined with strong encryption to securely store patient data. The survey found that healthcare professionals believe digitizing patient records and storing them in a centralized cloud system would improve access during emergencies and enable more efficient care compared to paper-based systems. However, ensuring privacy and security of patient data is paramount as healthcare incorporates these digital technologies.
Review on studies and research on widening of existing concrete bridgesIRJET Journal
This document summarizes several studies that have been conducted on widening existing concrete bridges. It describes a study from China that examined load distribution factors for a bridge widened with composite steel-concrete girders. It also outlines challenges and solutions for widening a bridge in the UAE, including replacing bearings and stitching the new and existing structures. Additionally, it discusses two bridge widening projects in New Zealand that involved adding precast beams and stitching to connect structures. Finally, safety measures and challenges for strengthening a historic bridge in Switzerland under live traffic are presented.
React based fullstack edtech web applicationIRJET Journal
The document describes the architecture of an educational technology web application built using the MERN stack. It discusses the frontend developed with ReactJS, backend with NodeJS and ExpressJS, and MongoDB database. The frontend provides dynamic user interfaces, while the backend offers APIs for authentication, course management, and other functions. MongoDB enables flexible data storage. The architecture aims to provide a scalable, responsive platform for online learning.
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...IRJET Journal
This paper proposes integrating Internet of Things (IoT) and blockchain technologies to help implement objectives of India's National Education Policy (NEP) in the education sector. The paper discusses how blockchain could be used for secure student data management, credential verification, and decentralized learning platforms. IoT devices could create smart classrooms, automate attendance tracking, and enable real-time monitoring. Blockchain would ensure integrity of exam processes and resource allocation, while smart contracts automate agreements. The paper argues this integration has potential to revolutionize education by making it more secure, transparent and efficient, in alignment with NEP goals. However, challenges like infrastructure needs, data privacy, and collaborative efforts are also discussed.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.IRJET Journal
This document provides a review of research on the performance of coconut fibre reinforced concrete. It summarizes several studies that tested different volume fractions and lengths of coconut fibres in concrete mixtures with varying compressive strengths. The studies found that coconut fibre improved properties like tensile strength, toughness, crack resistance, and spalling resistance compared to plain concrete. Volume fractions of 2-5% and fibre lengths of 20-50mm produced the best results. The document concludes that using a 4-5% volume fraction of coconut fibres 30-40mm in length with M30-M60 grade concrete would provide benefits based on previous research.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...IRJET Journal
The document discusses optimizing business management processes through automation using Microsoft Power Automate and artificial intelligence. It provides an overview of Power Automate's key components and features for automating workflows across various apps and services. The document then presents several scenarios applying automation solutions to common business processes like data entry, monitoring, HR, finance, customer support, and more. It estimates the potential time and cost savings from implementing automation for each scenario. Finally, the conclusion emphasizes the transformative impact of AI and automation tools on business processes and the need for ongoing optimization.
Multistoried and Multi Bay Steel Building Frame by using Seismic DesignIRJET Journal
The document describes the seismic design of a G+5 steel building frame located in Roorkee, India according to Indian codes IS 1893-2002 and IS 800. The frame was analyzed using the equivalent static load method and response spectrum method, and its response in terms of displacements and shear forces were compared. Based on the analysis, the frame was designed as a seismic-resistant steel structure according to IS 800:2007. The software STAAD Pro was used for the analysis and design.
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...IRJET Journal
This research paper explores using plastic waste as a sustainable and cost-effective construction material. The study focuses on manufacturing pavers and bricks using recycled plastic and partially replacing concrete with plastic alternatives. Initial results found that pavers and bricks made from recycled plastic demonstrate comparable strength and durability to traditional materials while providing environmental and cost benefits. Additionally, preliminary research indicates incorporating plastic waste as a partial concrete replacement significantly reduces construction costs without compromising structural integrity. The outcomes suggest adopting plastic waste in construction can address plastic pollution while optimizing costs, promoting more sustainable building practices.
Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapte...University of Maribor
Slides from talk presenting:
Aleš Zamuda: Presentation of IEEE Slovenia CIS (Computational Intelligence Society) Chapter and Networking.
Presentation at IcETRAN 2024 session:
"Inter-Society Networking Panel GRSS/MTT-S/CIS
Panel Session: Promoting Connection and Cooperation"
IEEE Slovenia GRSS
IEEE Serbia and Montenegro MTT-S
IEEE Slovenia CIS
11TH INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONIC AND COMPUTING ENGINEERING
3-6 June 2024, Niš, Serbia
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
A SYSTEMATIC RISK ASSESSMENT APPROACH FOR SECURING THE SMART IRRIGATION SYSTEMSIJNSA Journal
The smart irrigation system represents an innovative approach to optimize water usage in agricultural and landscaping practices. The integration of cutting-edge technologies, including sensors, actuators, and data analysis, empowers this system to provide accurate monitoring and control of irrigation processes by leveraging real-time environmental conditions. The main objective of a smart irrigation system is to optimize water efficiency, minimize expenses, and foster the adoption of sustainable water management methods. This paper conducts a systematic risk assessment by exploring the key components/assets and their functionalities in the smart irrigation system. The crucial role of sensors in gathering data on soil moisture, weather patterns, and plant well-being is emphasized in this system. These sensors enable intelligent decision-making in irrigation scheduling and water distribution, leading to enhanced water efficiency and sustainable water management practices. Actuators enable automated control of irrigation devices, ensuring precise and targeted water delivery to plants. Additionally, the paper addresses the potential threat and vulnerabilities associated with smart irrigation systems. It discusses limitations of the system, such as power constraints and computational capabilities, and calculates the potential security risks. The paper suggests possible risk treatment methods for effective secure system operation. In conclusion, the paper emphasizes the significant benefits of implementing smart irrigation systems, including improved water conservation, increased crop yield, and reduced environmental impact. Additionally, based on the security analysis conducted, the paper recommends the implementation of countermeasures and security approaches to address vulnerabilities and ensure the integrity and reliability of the system. By incorporating these measures, smart irrigation technology can revolutionize water management practices in agriculture, promoting sustainability, resource efficiency, and safeguarding against potential security threats.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
Comparative analysis between traditional aquaponics and reconstructed aquapon...bijceesjournal
The aquaponic system of planting is a method that does not require soil usage. It is a method that only needs water, fish, lava rocks (a substitute for soil), and plants. Aquaponic systems are sustainable and environmentally friendly. Its use not only helps to plant in small spaces but also helps reduce artificial chemical use and minimizes excess water use, as aquaponics consumes 90% less water than soil-based gardening. The study applied a descriptive and experimental design to assess and compare conventional and reconstructed aquaponic methods for reproducing tomatoes. The researchers created an observation checklist to determine the significant factors of the study. The study aims to determine the significant difference between traditional aquaponics and reconstructed aquaponics systems propagating tomatoes in terms of height, weight, girth, and number of fruits. The reconstructed aquaponics system’s higher growth yield results in a much more nourished crop than the traditional aquaponics system. It is superior in its number of fruits, height, weight, and girth measurement. Moreover, the reconstructed aquaponics system is proven to eliminate all the hindrances present in the traditional aquaponics system, which are overcrowding of fish, algae growth, pest problems, contaminated water, and dead fish.
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
A review on techniques and modelling methodologies used for checking electrom...nooriasukmaningtyas
The proper function of the integrated circuit (IC) in an inhibiting electromagnetic environment has always been a serious concern throughout the decades of revolution in the world of electronics, from disjunct devices to today’s integrated circuit technology, where billions of transistors are combined on a single chip. The automotive industry and smart vehicles in particular, are confronting design issues such as being prone to electromagnetic interference (EMI). Electronic control devices calculate incorrect outputs because of EMI and sensors give misleading values which can prove fatal in case of automotives. In this paper, the authors have non exhaustively tried to review research work concerned with the investigation of EMI in ICs and prediction of this EMI using various modelling methodologies and measurement setups.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.