Browsing by Author "Ghosh, Pronab"

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A Comparative Study of Different Machine Learning Tools in Detecting Diabetes
(Scopus, 2021) Ghosh, Pronab; Azam, , Sami; Karim, Asif; Hassan, Mehedi; Roy, Kuber; Jonkman, Mirjam
A significant proportion of people around the world are currently suffering from the harmful effects of diabetes and a considerable number of them not being identified at an early stage. Over time this may result in serious health problem such as blindness and kidney failure. To accurately classify the disease, different machine learning (ML) approaches can be utilized. In this context, four separate ML algorithms, namely Gradient Boosting (GB), Support Vector Machine (SVM) AdaBoost (AB), and Random Forest (RF) are evaluated using the Pima Indians diabetes dataset, first with based on all features, then to the features selected with the Minimal Redundancy Maximal Relevance (MRMR) Feature Selection (FS) approach. Seven different types of performance evaluation metrics were computed with a 10-fold cross-validation (CV) approach. Computational complexity is also evaluated. The best results were obtained with the Random Forest approach, achieving an accuracy of 99.35%.
A Comparative Study of Different Machine Learning Tools in Detecting Diabetes
(Scopus, 2021) Ghosh, Pronab; Azam, Sami; Karim, Asif; Hassan, Mehedi; Roy, Kuber; Jonkman, Mirjam
A significant proportion of people around the world are currently suffering from the harmful effects of diabetes and a considerable number of them not being identified at an early stage. Over time this may result in serious health problem such as blindness and kidney failure. To accurately classify the disease, different machine learning (ML) approaches can be utilized. In this context, four separate ML algorithms, namely Gradient Boosting (GB), Support Vector Machine (SVM) AdaBoost (AB), and Random Forest (RF) are evaluated using the Pima Indians diabetes dataset, first with based on all features, then to the features selected with the Minimal Redundancy Maximal Relevance (MRMR) Feature Selection (FS) approach. Seven different types of performance evaluation metrics were computed with a 10-fold cross-validation (CV) approach. Computational complexity is also evaluated. The best results were obtained with the Random Forest approach, achieving an accuracy of 99.35%.
A Comparative Study on Different Machine Learning Algorithms for Achieving Accurate Prediction for Heart Diseases
(Daffodil International University, 2018-11-27) Ghosh, Pronab; Ahmed, Khobayeb; Karmaker, Madhob
Over the years, heart diseases have become one of the most common causes related to death. Most of the time heart diseases are detected at the very last stage; therefore, an accurate prediction may reduce the catastrophe related to heart diseases. Heart-related diseases have a significant relationship with various health features including age, sex, heartbeat rate, blood pressure, cholesterol etc. In this context, four machine learning algorithms (e.g. Multiple Linear Regression, Decision Tree, Random Forest and Support Vector Machine) are applied on Cleveland heart disease dataset to analyze the comparative performance for achieving accurate prediction. The dataset contains thirteen health features, which have significant relations to heart disease. The best prediction has been achieved by the Random Forest algorithm, which is an ensemble version of the Decision Tree algorithm. To recapitulate the Random Forest algorithm outperformed other three algorithms followed by Support Vector Machine algorithm by providing a satisfactory prediction on 303 patient’s data.
A Performance Based Study on Deep Learning Algorithms in the Effective Prediction of Breast Cancer
(2021 International Joint Conference on Neural Networks (IJCNN), IEEE, 2021-09-21) Ghosh, Pronab; Azam, Sami; Hasib, Khan Md.; Karim, Asif; Jonkman, Mirjam; Anwar, Adnan
Breast Cancer is one of the leading causes of death worldwide. Early detection is very important in increasing survival rates. Intensive research is therefore done to improve early detection of such cancers through the use of available technology. This includes various image processing techniques andgeneral machine learning. However, the reported accuracy for many of these studies was often not at the desirable level. Deep Learning based techniques are a promising approach for the early detection of Breast Cancer. We have therefore done a comparative analysis of seven Deep Learning techniques applied to the Wisconsin Breast Cancer (Diagnostic) Dataset. Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) were proven to be the most effective algorithms as these have demonstrated good results for the majority of performance indicators used in this study, including an accuracy of over 99 percent.
A Shallow Deep Learning Approach To Classify Skin Cancer Using Down-Scaling Method To Minimize Time and Space Complexity
(Daffodil International University, 2022-08-04) Sidratul, Montaha; Azam, Sami; Rafid, A. K. M. Rakibul Haque; Islam, Sayma; Ghosh, Pronab; Jonkman, Mirjam
The complex feature characteristics and low contrast of cancer lesions, a high degree of inter-class resemblance between malignant and benign lesions, and the presence of various artifacts including hairs make automated melanoma recognition in dermoscopy images quite challenging. To date, various computer-aided solutions have been proposed to identify and classify skin cancer. In this paper, a deep learning model with a shallow architecture is proposed to classify the lesions into benign and malignant. To achieve effective training while limiting over fitting problems due to limited training data, image preprocessing and data augmentation processes are introduced. After this, the ‘box blur’ down-scaling method is employed, which adds efficiency to our study by reducing the overall training time and space complexity significantly. Our proposed shallow convolutional neural network (SCNN_12) model is trained and evaluated on the Kaggle skin cancer data ISIC archive which was augmented to 16485 images by implementing different augmentation techniques. The model was able to achieve an accuracy of 98.87% with optimizer Adam and a learning rate of 0.001. In this regard, parameter and hyper-parameters of the model are determined by performing ablation studies. To assert no occurrence of overfitting, experiments are carried out exploring k-fold cross-validation and different dataset split ratios. Furthermore, to affirm the robustness the model is evaluated on noisy data to examine the performance when the image quality gets corrupted. This research corroborates that effective training for medical image analysis, addressing training time and space complexity, is possible even with a light weighted network using a limited amount of training data.
A Variable Length Key Based Cryptographic Approach on Cloud Data
(Scopus, 2019-12-21) Ghosh, Pronab; Jabiullah, Md. Ismail; Hasan, Md. Zahid; Atik, Syeda Tanjila
Security has emerged to be a concerning issue in cloud computing as numerous sensitive data are processed and transferred over the cloud servers. In this paper, a variable length key based security mechanism has been designed, developed, and implemented by using the Advanced Encryption Standard (AES) protocol for the secured delivery of cloud data which also works as SaaS (Software as a Services). The length of the used key is given as input by the user followed by the selection of a secret key which is fed into the AES cryptographic system with the desired cloud data message thus producing the Cipher text that is to be transmitted to the destination. In the receiver end, the reverse process is performed with the same key on the received Cipher text and the plaintext is retrieved. Using Python to implement and validate the security process, several messages from cloud users are used and the result for each input is analyzed. The result of our study is then compared with the two existing approaches which clearly shows the advancement of the proposed approach. This process can be applied in any secured electronic message transactions for cloud data.
A Web Based Application for Agriculture
(International Journal of Emerging Trends in Engineering Research, 2020-06) Shamrat, F. M. Javed Mehedi; Asaduzzaman, Md; Ghosh, Pronab; Sultan, Md Dipu; Tasnim, Zarrin
Bangladesh is predominantly an agricultural country, where agriculture sector plays a vital role in accelerating the economic growth. Agriculture remains the most important sector of Bangladeshi economy, contributing 19.6 percent to the national GDP and providing employment for 63 percent of the population. A National Agricultural Census report has said Bangladesh is currently home to 16.5 million farmer families. The report also highlighted the fact that there over four million landless farmers, with near 6.8 million farmers cultivating other people's land. To help the farmers and improve in agricultural sector, we design and develop a web based application "Smart Farming System". Farmers of Bangladesh can learn and share various knowledge and problem facing during farming through this system. Farmers can acquire information around various diseases and resolver on their problems. They can get support in various agricultural activities, by the help of the consultants and doctors through the "Smart Farming System". To develop this system we used HTML5, CSS, Bootstrap, and JavaScript. In addition, the PHP framework is used to manage the MySQL database. In testing phase, we tested it with a community based social media on Facebook and its work great with expecting output, peoples are expecting these services to be interesting.
AlzheimerNet: An Effective Deep Learning Based Proposition for Alzheimer’s Disease Stages Classification From Functional Brain Changes in Magnetic Resonance Images
(IEEE, 2023-02-14) Shamrat, F M Javed Mehedi; Akter, Shamima; Azam, Sami; Karim, Asif; Ghosh, Pronab; Hasib, Khan Md.; Boer, Frisode; Ahmed, Kawsar
Alzheimer’s disease is largely the underlying cause of dementia due to its progressive neurodegenerative nature among the elderly. The disease can be divided into five stages: Subjective Memory Concern (SMC), Mild Cognitive Impairment (MCI), Early MCI (EMCI), Late MCI (LMCI), and Alzheimer’s Disease (AD). Alzheimer’s disease is conventionally diagnosed using an MRI scan of the brain. In this research, we propose a fine-tuned convolutional neural network (CNN) classifier called AlzheimerNet, which can identify all five stages of Alzheimer’s disease and the Normal Control (NC) class. The ADNI database’s MRI scan dataset is obtained for use in training and testing the proposed model. To prepare the raw data for analysis, we applied the CLAHE image enhancement method. Data augmentation was used to remedy the unbalanced nature of the dataset and the resultant dataset consisted of 60000 image data on the 6 classes. Initially, five existing models including VGG16, MobileNetV2, AlexNet, ResNet50 and InceptionV3 were trained and tested to achieve test accuracies of 78.84%, 86.85%, 78.87%, 80.98% and 96.31% respectively. Since InceptionV3 provides the highest accuracy, this model is later modified to design the AlzheimerNet using RMSprop optimizer and learning rate 0.00001 to achieve the highest test accuracy of 98.67%. The five pre-trained models and the proposed fine-tuned model were compared in terms of various performance matrices to demonstrate whether the AlzheimerNet model is in fact performing better in classifying and detecting the six classes. An ablation study shows the hyperparameters used in the experiment. The suggested model outperforms the traditional methods for classifying Alzheimer’s disease stages from brain MRI, as measured by a two-tailed Wilcoxon signed-rank test, with a significance of < 0.05.
An Intelligent Thyroid Diagnosis System Utilising Multiple Ensemble and Explainable Algorithms with Medical Supported Attributes
(Elsevier, 2023-01-15) Sutradhar, Ananda; Al Rafi, Mustahsin; Ghosh, Pronab; Shamrat, F. M.Javed Mehedi; Moniruzzaman, Md.; Ahmed, Kawsar; Azad, AKM; Bui, Francis M.; Chen, Li; Moni, Mohammad Ali
The widespread impact of thyroid disease and its diagnosis is a challenging task for healthcare experts. The conventional technique for predicting such a vital disease is complex and time-consuming. A data-driven approach may offer predictive solutions, but it relies on all relevant attributes, which are computationally expensive. Hence, we propose a novel machine learning (ML) based disease prediction system that could potentially predict it by considering three crucial steps. First, to reduce the dimension of the dataset, three feature selection techniques were employed, including Feature Importance (FIS), Information Gain Selections (IGS), and Least Absolute Shrinkage and Selection Operator (LAS). Moreover, recommended medical references were considered while developing a feature set having the identical attributes as High-Risk Factors (HRF). Second, the models, including the Three Stage Hybrid Classifier (3SHC) and the Three Stage Hybrid Artificial Neural Network (3SHANN), are used as classifiers on the training data set. Third, a Local Interpretable Model-agnostic Explanations (LIME) to the 3SHC with the HRF samples was applied to individually explain the predictions. Then, the overall behaviors of both gender and age categories were explored with the help of a Partial Dependence Plot (PDP). Finally, the proposed system is validated with extensive experiments where the 3SHC achieves an accuracy (ACC) of 99.29%, which can play a crucial role in preventing thyroid disease and alleviating stress in the healthcare sector.
BOO-ST and CBCEC: Two Novel Hybrid Machine Learning Methods Aim To Reduce the Mortality of Heart Failure Patients
(Springer Nature Limited, 2023-12-18) Sutradhar, Ananda; Al Rafi, Mustahsin; Shamrat, F M Javed Mehedi; Ghosh, Pronab; Das, Subrata; Islam, Md Anaytul; Ahmed, Kawsar; Zhou, Xujuan; Azad, A. K. M.; Alyami, Salem A.; Moni, Mohammad Ali
Heart failure (HF) is a leading cause of mortality worldwide. Machine learning (ML) approaches have shown potential as an early detection tool for improving patient outcomes. Enhancing the effectiveness and clinical applicability of the ML model necessitates training an efficient classifier with a diverse set of high-quality datasets. Hence, we proposed two novel hybrid ML methods ((a) consisting of Boosting, SMOTE, and Tomek links (BOO-ST); (b) combining the best-performing conventional classifier with ensemble classifiers (CBCEC)) to serve as an efficient early warning system for HF mortality. The BOO-ST was introduced to tackle the challenge of class imbalance, while CBCEC was responsible for training the processed and selected features derived from the Feature Importance (FI) and Information Gain (IG) feature selection techniques. We also conducted an explicit and intuitive comprehension to explore the impact of potential characteristics correlating with the fatality cases of HF. The experimental results demonstrated the proposed classifier CBCEC showcases a significant accuracy of 93.67% in terms of providing the early forecasting of HF mortality. Therefore, we can reveal that our proposed aspects (BOO-ST and CBCEC) can be able to play a crucial role in preventing the death rate of HF and reducing stress in the healthcare sector.
Breastnet18
(Biology, 2021-11-13) Montaha, Sidratul; Azam, Sami; Muhammad Rakibul Haque Rafid, Abul Kalam; Ghosh, Pronab; Hasan, Md. Zahid; Jonkman, Mirjam; De Boer, Friso
Background: Identification and treatment of breast cancer at an early stage can reduce mortality. Currently, mammography is the most widely used effective imaging technique in breast cancer detection. However, an erroneous mammogram based interpretation may result in false diagnosis rate, as distinguishing cancerous masses from adjacent tissue is often complex and error-prone. Methods: Six pre-trained and fine-tuned deep CNN architectures: VGG16, VGG19, MobileNetV2, ResNet50, DenseNet201, and InceptionV3 are evaluated to determine which model yields the best performance. We propose a BreastNet18 model using VGG16 as foundational base, since VGG16 performs with the highest accuracy. An ablation study is performed on BreastNet18, to evaluate its robustness and achieve the highest possible accuracy. Various image processing techniques with suitable parameter values are employed to remove artefacts and increase the image quality. A total dataset of 1442 preprocessed mammograms was augmented using seven augmentation techniques, resulting in a dataset of 11,536 images. To investigate possible over fitting issues, a k-fold cross validation is carried out. The model was then tested on noisy mammograms to evaluate its robustness. Results were compared with previous studies. Results: Proposed BreastNet18 model performed best with a training accuracy of 96.72%, a validating accuracy of 97.91%, and a test accuracy of 98.02%. In contrast to this, VGGNet19 yielded test accuracy of 96.24%, MobileNetV2 77.84%, ResNet50 79.98%, DenseNet201 86.92%, and InceptionV3 76.87%. Conclusions: Our proposed approach based on image processing, transfer learning, fine-tuning, and ablation study has demonstrated a high correct breast cancer classification while dealing with a limited number of complex medical images.
Early Prediction of Chronic Kidney Disease
(Daffodil International University, 22-08-29) Mondol, Chaity; Shamrat, F. M. Javed Mehedi; Hasan, Md. Robiul; Alam, Saidul; Ghosh, Pronab; Tasnim, Zarrin; Ahmed, Kawsar; Bui, Francis M.; Ibrahim, Sobhy M.
Chronic kidney disease (CKD) is one of the most life-threatening disorders. To improve survivability, early discovery and good management are encouraged. In this paper, CKD was diagnosed using multiple optimized neural networks against traditional neural networks on the UCI machine learning dataset, to identify the most efficient model for the task. The study works on the binary classification of CKD from 24 attributes. For classification, optimized CNN (OCNN), ANN (OANN), and LSTM (OLSTM) models were used as well as traditional CNN, ANN, and LSTM models. With various performance matrixes, error measures, loss values, AUC values, and compilation time, the implemented models are compared to identify the most competent model for the classification of CKD. It is observed that, overall, the optimized models have better performance compared to the traditional models. The highest validation accuracy among the tradition models were achieved from CNN with 92.71%, whereas OCNN, OANN, and OLSTM have higher accuracies of 98.75%, 96.25%, and 98.5%, respectively. Additionally, OCNN has the highest AUC score of 0.99 and the lowest compilation time for classification with 0.00447 s, making it the most efficient model for the diagnosis of CKD.
Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms with Relief and LASSO Feature Selection Techniques
(IEEE, 2021-01-22) Ghosh, Pronab; Azam, Sami; Jonkman, Mirjam; Karim, Asif; Shamrat, F. M. Javed Mehedi; Ignatious, Eva; Shultana, Shahana
Cardiovascular diseases (CVD) are among the most common serious illnesses affecting human health. CVDs may be prevented or mitigated by early diagnosis, and this may reduce mortality rates. Identifying risk factors using machine learning models is a promising approach. We would like to propose a model that incorporates different methods to achieve effective prediction of heart disease. For our proposed model to be successful, we have used efficient Data Collection, Data Pre-processing and Data Transformation methods to create accurate information for the training model. We have used a combined dataset (Cleveland, Long Beach VA, Switzerland, Hungarian and Stat log). Suitable features are selected by using the Relief, and Least Absolute Shrinkage and Selection Operator (LASSO) techniques. New hybrid classifiers like Decision Tree Bagging Method (DTBM), Random Forest Bagging Method (RFBM), K-Nearest Neighbors Bagging Method (KNNBM), AdaBoost Boosting Method (ABBM), and Gradient Boosting Boosting Method (GBBM) are developed by integrating the traditional classifiers with bagging and boosting methods, which are used in the training process. We have also instrumented some machine learning algorithms to calculate the Accuracy (ACC), Sensitivity (SEN), Error Rate, Precision (PRE) and F1 Score (F1) of our model, along with the Negative Predictive Value (NPR), False Positive Rate (FPR), and False Negative Rate (FNR). The results are shown separately to provide comparisons. Based on the result analysis, we can conclude that our proposed model produced the highest accuracy while using RFBM and Relief feature selection methods (99.05%).
Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms with Relief and Lasso Feature Selection Techniques
(Scopus, 2021) Ghosh, Pronab; Azam, Sami; Jonkman, Mirjam; Karim, Asif; Shamrat, F. M. Javed Mehedi
Cardiovascular diseases (CVD) are among the most common serious illnesses affecting human health. CVDs may be prevented or mitigated by early diagnosis, and this may reduce mortality rates. Identifying risk factors using machine learning models is a promising approach. We would like to propose a model that incorporates different methods to achieve effective prediction of heart disease. For our proposed model to be successful, we have used efficient Data Collection, Data Pre-processing and Data Transformation methods to create accurate information for the training model. We have used a combined dataset (Cleveland, Long Beach VA, Switzerland, Hungarian and Stat log). Suitable features are selected by using the Relief, and Least Absolute Shrinkage and Selection Operator (LASSO) techniques. New hybrid classifiers like Decision Tree Bagging Method (DTBM), Random Forest Bagging Method (RFBM), K-Nearest Neighbors Bagging Method (KNNBM), AdaBoost Boosting Method (ABBM), and Gradient Boosting Boosting Method (GBBM) are developed by integrating the traditional classifiers with bagging and boosting methods, which are used in the training process. We have also instrumented some machine learning algorithms to calculate the Accuracy (ACC), Sensitivity (SEN), Error Rate, Precision (PRE) and F1 Score (F1) of our model, along with the Negative Predictive Value (NPR), False Positive Rate (FPR), and False Negative Rate (FNR). The results are shown separately to provide comparisons. Based on the result analysis, we can conclude that our proposed model produced the highest accuracy while using RFBM and Relief feature selection methods (99.05%).
Expert Cancer Model Using Supervised Algorithms with a Lasso Selection Approach
(International Journal of Electrical and Computer Engineering (IJECE), 2021) Ghosh, Pronab; Karim, Asif; Atik, Syeda Tanjila; Afrin, Saima; Saifuzzaman, Mohd.
One of the most critical issues of the mortality rate in the medical field in current times is breast cancer. Nowadays, a large number of men and women is facing cancer-related deaths due to the lack of early diagnosis systems and proper treatment per year. To tackle the issue, various data mining approaches have been analyzed to build an effective model that helps to identify the different stages of deadly cancers. The study successfully proposes an early cancer disease model based on five different supervised algorithms such as logistic regression (henceforth LR), decision tree (henceforth DT), random forest (henceforth RF), Support vector machine (henceforth SVM), and K-nearest neighbor (henceforth KNN). After an appropriate preprocessing of the dataset, least absolute shrinkage and selection operator (LASSO) was used for feature selection (FS) using a 10-fold cross-validation (CV) approach. Employing LASSO with 10-fold cross-validation has been a novel steps introduced in this research. Afterwards, different performance evaluation metrics were measured to show accurate predictions based on the proposed algorithms. The result indicated top accuracy was received from RF classifier, approximately 99.41% with the integration of LASSO. Finally, a comprehensive comparison was carried out on Wisconsin breast cancer (diagnostic) dataset (WBCD) together with some current works containing all features.
Expert Cancer Model Using Supervised Algorithms with a Lasso Selection Approach
(International Journal of Electrical and Computer Engineering (IJECE), Elsevier, 2021) Ghosh, Pronab; Karim, Asif; Atik, Syeda Tanjila; Afrin, Saima; Saifuzzaman, Mohd.
One of the most critical issues of the mortality rate in the medical field in current times is breast cancer. Nowadays, a large number of men and women is facing cancer-related deaths due to the lack of early diagnosis systems and proper treatment per year. To tackle the issue, various data mining approaches have been analyzed to build an effective model that helps to identify the different stages of deadly cancers. The study successfully proposes an early cancer disease model based on five different supervised algorithms such as logistic regression (henceforth LR), decision tree (henceforth DT), random forest (henceforth RF), Support vector machine (henceforth SVM), and K-nearest neighbor (henceforth KNN). After an appropriate preprocessing of the dataset, least absolute shrinkage and selection operator (LASSO) was used for feature selection (FS) using a 10-fold cross-validation (CV) approach. Employing LASSO with 10-fold cross-validation has been a novel steps introduced in this research. Afterwards, different performance evaluation metrics were measured to show accurate predictions based on the proposed algorithms. The result indicated top accuracy was received from RF classifier, approximately 99.41% with the integration of LASSO. Finally, a comprehensive comparison was carried out on Wisconsin breast cancer (diagnostic) dataset (WBCD) together with some current works containing all features.
Human Face Recognition Using Eigenface, SURF Method
(Springer, 2022-01-01) Shamrat, F.M. Javed Mehedi; Ghosh, Pronab; Tasnim, Zarrin; Khan, Aliza Ahmed; Uddin, Md. Shihab; Chowdhury, Tahmid Rashik
One such complicated and exciting problem in computer vision and pattern recognition is identification using face biometrics. One such application of biometrics, used in video inspection, biometric authentication, surveillance, and so on, is facial recognition. Many techniques for detecting facial biometrics have been studied in the past three years. However, considerations such as shifting lighting, landscape, the nose being farther from the camera, the background being farther from the camera creating blurring, and noise present renders the previous approaches bad. To solve these problems, numerous works with sufficient clarification on this research subject have been introduced in this paper. This paper analyzes the multiple methods researchers use in their numerous researches to solve different types of problems faced during facial recognition. A new technique is implemented to investigate the feature space to the abstract component subset. Principle component analysis (PCA) is used to analyze the features and uses speed up robust features (SURF) technique, eigenfaces, identification, and matching is done, respectively. Thus, we get improved accuracy and almost similar recognition rate from the acquired research results based on the facial image dataset, which has been taken from the ORL database.
Implementation of Machine Learning Algorithms to Detect the Prognosis Rate of Kidney Disease
(2020 IEEE International Conference for Innovation in Technology (INOCON) , IEEE, 2020-11) Shamrat, F.M. Javed Mehedi; Ghosh, Pronab; Sadek, Mahbubul Hasan; Kazi, Md. Aslam; Shultana, Shahana
The chronic kidney disease is the loss of kidney function. Often time, the symptoms of the disease is not noticeable and a significant amount of lives are lost annually due to the disease. Using machine learning algorithm for medical studies, the disease can be predicted with a high accuracy rate and a very short time. Using four of the supervised classification learning algorithms, i.e., logistic regression, Decision tree, Random Forest and KNN algorithms, the prediction of the disease can be done. In the paper, the performance of the predictions of the algorithms are analyzed using a pre-processed dataset. The performance analysis is done base on the accuracy of the results, prediction time, ROC and AUC Curve and error rate. The comparison of the algorithms will suggest which algorithm is best fit for predicting the chronic kidney disease.
Mitigating the Latency Induced Delay in IP Telephony Through an Enhanced De-Jitter Buffer
(Springer, 2022-07-21) Karim, Asif; Ahmed, Eshtiak; Azam, Sami; Ghosh, Pronab
IP telephony or voice over IP (VoIP) at present is promising a shining future for voice services. There are several technical aspects which make the technology attractive; on the other hand, few technical loopholes and shortcomings make user’s experience less than optimal and also bring forth significant security issues. This paper offers a technical dissection of the quality of service (QoS) of VoIP. “Signaling” part of VoIP has been discussed based on the Session Initiation Protocol (SIP) along with propositions to tackle problem like jitter that often causes latency in communication. To address the issue of jittering, an alteration in the working mechanism of de-jitter buffer has been put forward where it is shown that addition of few extra variables within the de-jitter buffer to synchronize the packet arrival and release timing can certainly improve the user experience. Reducing the latency is of prime importance to voice data services as it directly affects the acceptance trend of VoIP among mass consumers. The scale of improvement has also been compared to that of a normal jitter buffer as well as a detailed illustration has been provided on Session Initiation Protocol (SIP), a key component of the overall system that makes thing happen. The proposed modification in the de-jitter buffer has been illustrated along with positive results. It shows a one-third improvement in the average latency, resulting into twice as better performance and nearly halved latency.
Optimization of Prediction Method of Chronic Kidney Disease Using Machine Learning Algorithm
(2020 15th International Joint Symposium on Artificial Intelligence and Natural Language Processing (iSAI-NLP), IEEE, 2020-11-18) Ghosh, Pronab; Shamrat, F. M. Javed Mehedi; Shultana, Shahana; Afrin, Saima; Anjum, Atqiya Abida; Khan, Aliza Ahmed
Chronic Kidney disease (CKD), a slow and late-diagnosed disease, is one of the most important problems of mortality rate in the medical sector nowadays. Based on this critical issue, a significant number of men and women are now suffering due to the lack of early screening systems and appropriate care each year. However, patients' lives can be saved with the fast detection of disease in the earliest stage. In addition, the evaluation process of machine learning algorithm can detect the stage of this deadly disease much quicker with a reliable dataset. In this paper, the overall study has been implemented based on four reliable approaches, such as Support Vector Machine (henceforth SVM), AdaBoost (henceforth AB), Linear Discriminant Analysis (henceforth LDA), and Gradient Boosting (henceforth GB) to get highly accurate results of prediction. These algorithms are implemented on an online dataset of UCI machine learning repository. The highest predictable accuracy is obtained from Gradient Boosting (GB) Classifiers which is about to 99.80% accuracy. Later, different performance evaluation metrics have also been displayed to show appropriate outcomes. To end with, the most efficient and optimized algorithms for the proposed job can be selected depending on these benchmarks.