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  1. Article: Monitoring time domain characteristics of Parkinson's disease using 3D memristive neuromorphic system.

    Siddique, Md Abu Bakr / Zhang, Yan / An, Hongyu

    Frontiers in computational neuroscience

    2023  Volume 17, Page(s) 1274575

    Abstract: Introduction: Parkinson's disease (PD) is a neurodegenerative disorder affecting millions of patients. Closed-Loop Deep Brain Stimulation (CL-DBS) is a therapy that can alleviate the symptoms of PD. The CL-DBS system consists of an electrode sending ... ...

    Abstract Introduction: Parkinson's disease (PD) is a neurodegenerative disorder affecting millions of patients. Closed-Loop Deep Brain Stimulation (CL-DBS) is a therapy that can alleviate the symptoms of PD. The CL-DBS system consists of an electrode sending electrical stimulation signals to a specific region of the brain and a battery-powered stimulator implanted in the chest. The electrical stimuli in CL-DBS systems need to be adjusted in real-time in accordance with the state of PD symptoms. Therefore, fast and precise monitoring of PD symptoms is a critical function for CL-DBS systems. However, the current CL-DBS techniques suffer from high computational demands for real-time PD symptom monitoring, which are not feasible for implanted and wearable medical devices.
    Methods: In this paper, we present an energy-efficient neuromorphic PD symptom detector using memristive three-dimensional integrated circuits (3D-ICs). The excessive oscillation at beta frequencies (13-35 Hz) at the subthalamic nucleus (STN) is used as a biomarker of PD symptoms.
    Results: Simulation results demonstrate that our neuromorphic PD detector, implemented with an 8-layer spiking Long Short-Term Memory (S-LSTM), excels in recognizing PD symptoms, achieving a training accuracy of 99.74% and a validation accuracy of 99.52% for a 75%-25% data split. Furthermore, we evaluated the improvement of our neuromorphic CL-DBS detector using NeuroSIM. The chip area, latency, energy, and power consumption of our CL-DBS detector were reduced by 47.4%, 66.63%, 65.6%, and 67.5%, respectively, for monolithic 3D-ICs. Similarly, for heterogeneous 3D-ICs, employing memristive synapses to replace traditional Static Random Access Memory (SRAM) resulted in reductions of 44.8%, 64.75%, 65.28%, and 67.7% in chip area, latency, and power usage.
    Discussion: This study introduces a novel approach for PD symptom evaluation by directly utilizing spiking signals from neural activities in the time domain. This method significantly reduces the time and energy required for signal conversion compared to traditional frequency domain approaches. The study pioneers the use of neuromorphic computing and memristors in designing CL-DBS systems, surpassing SRAM-based designs in chip design area, latency, and energy efficiency. Lastly, the proposed neuromorphic PD detector demonstrates high resilience to timing variations in brain neural signals, as confirmed by robustness analysis.
    Language English
    Publishing date 2023-12-15
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2452964-3
    ISSN 1662-5188
    ISSN 1662-5188
    DOI 10.3389/fncom.2023.1274575
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Book ; Online: Unsupervised Segmentation Algorithms' Implementation in ITK for Tissue Classification via Human Head MRI Scans

    Sakib, Shadman / Siddique, Md. Abu Bakr

    2019  

    Abstract: Tissue classification is one of the significant tasks in the field of biomedical image analysis. Magnetic Resonance Imaging (MRI) is of great importance in tissue classification especially in the areas of brain tissue classification which is able to ... ...

    Abstract Tissue classification is one of the significant tasks in the field of biomedical image analysis. Magnetic Resonance Imaging (MRI) is of great importance in tissue classification especially in the areas of brain tissue classification which is able to recognize anatomical areas of interest such as surgical planning, monitoring therapy, clinical drug trials, image registration, stereotactic neurosurgery, radiotherapy etc. The task of this paper is to implement different unsupervised classification algorithms in ITK and perform tissue classification (white matter, gray matter, cerebrospinal fluid (CSF) and background of the human brain). For this purpose, 5 grayscale head MRI scans are provided. In order of classifying brain tissues, three algorithms are used. These are: Otsu thresholding, Bayesian classification and Bayesian classification with Gaussian smoothing. The obtained classification results are analyzed in the results and discussion section.

    Comment: 4 Pages, 2 Tables
    Keywords Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Distributed ; Parallel ; and Cluster Computing
    Subject code 006
    Publishing date 2019-02-26
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Book ; Online: Performance Evaluation of t-SNE and MDS Dimensionality Reduction Techniques with KNN, ENN and SVM Classifiers

    Sakib, Shadman / Siddique, Md. Abu Bakr / Rahman, Md. Abdur

    2020  

    Abstract: The central goal of this paper is to establish two commonly available dimensionality reduction (DR) methods i.e. t-distributed Stochastic Neighbor Embedding (t-SNE) and Multidimensional Scaling (MDS) in Matlab and to observe their application in several ... ...

    Abstract The central goal of this paper is to establish two commonly available dimensionality reduction (DR) methods i.e. t-distributed Stochastic Neighbor Embedding (t-SNE) and Multidimensional Scaling (MDS) in Matlab and to observe their application in several datasets. These DR techniques are applied to nine different datasets namely CNAE9, Segmentation, Seeds, Pima Indians diabetes, Parkinsons, Movement Libras, Mammographic Masses, Knowledge, and Ionosphere acquired from UCI machine learning repository. By applying t-SNE and MDS algorithms, each dataset is transformed to the half of its original dimension by eliminating unnecessary features from the datasets. Subsequently, these datasets with reduced dimensions are fed into three supervised classification algorithms for classification. These classification algorithms are K Nearest Neighbors (KNN), Extended Nearest Neighbors (ENN), and Support Vector Machine (SVM). Again, all these algorithms are implemented in Matlab. The training and test data ratios are maintained as ninety percent: ten percent for each dataset. Upon accuracy observation, the efficiency for every dimensionality technique with availed classification algorithms is analyzed and the performance of each classifier is evaluated.

    Comment: 2020 IEEE Region 10 Symposium (TENSYMP), 5-7 June 2020, Dhaka, Bangladesh
    Keywords Computer Science - Machine Learning ; Statistics - Machine Learning
    Subject code 006
    Publishing date 2020-06-20
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: Implementation of Fruits Recognition Classifier using Convolutional Neural Network Algorithm for Observation of Accuracies for Various Hidden Layers

    Sakib, Shadman / Ashrafi, Zahidun / Siddique, Md. Abu Bakr

    2019  

    Abstract: Fruit recognition using Deep Convolutional Neural Network (CNN) is one of the most promising applications in computer vision. In recent times, deep learning based classifications are making it possible to recognize fruits from images. However, fruit ... ...

    Abstract Fruit recognition using Deep Convolutional Neural Network (CNN) is one of the most promising applications in computer vision. In recent times, deep learning based classifications are making it possible to recognize fruits from images. However, fruit recognition is still a problem for the stacked fruits on weighing scale because of the complexity and similarity. In this paper, a fruit recognition system using CNN is proposed. The proposed method uses deep learning techniques for the classification. We have used Fruits-360 dataset for the evaluation purpose. From the dataset, we have established a dataset which contains 17,823 images from 25 different categories. The images are divided into training and test dataset. Moreover, for the classification accuracies, we have used various combinations of hidden layer and epochs for different cases and made a comparison between them. The overall performance losses of the network for different cases also observed. Finally, we have achieved the best test accuracy of 100% and a training accuracy of 99.79%.

    Comment: 4 Pages, 5 Figures
    Keywords Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2019-04-01
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: Recognition of Handwritten Digit using Convolutional Neural Network in Python with Tensorflow and Comparison of Performance for Various Hidden Layers

    Siddique, Fathma / Sakib, Shadman / Siddique, Md. Abu Bakr

    2019  

    Abstract: In recent times, with the increase of Artificial Neural Network (ANN), deep learning has brought a dramatic twist in the field of machine learning by making it more artificially intelligent. Deep learning is remarkably used in vast ranges of fields ... ...

    Abstract In recent times, with the increase of Artificial Neural Network (ANN), deep learning has brought a dramatic twist in the field of machine learning by making it more artificially intelligent. Deep learning is remarkably used in vast ranges of fields because of its diverse range of applications such as surveillance, health, medicine, sports, robotics, drones, etc. In deep learning, Convolutional Neural Network (CNN) is at the center of spectacular advances that mixes Artificial Neural Network (ANN) and up to date deep learning strategies. It has been used broadly in pattern recognition, sentence classification, speech recognition, face recognition, text categorization, document analysis, scene, and handwritten digit recognition. The goal of this paper is to observe the variation of accuracies of CNN to classify handwritten digits using various numbers of hidden layers and epochs and to make the comparison between the accuracies. For this performance evaluation of CNN, we performed our experiment using Modified National Institute of Standards and Technology (MNIST) dataset. Further, the network is trained using stochastic gradient descent and the backpropagation algorithm.

    Comment: To be published in 5th International Conference on Advances in Electrical Engineering (ICAEE-2019)
    Keywords Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Neural and Evolutionary Computing
    Subject code 006
    Publishing date 2019-09-12
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Book ; Online: Performance Analysis of Deep Autoencoder and NCA Dimensionality Reduction Techniques with KNN, ENN and SVM Classifiers

    Siddique, Md. Abu Bakr / Sakib, Shadman / Rahman, Md. Abdur

    2019  

    Abstract: The central aim of this paper is to implement Deep Autoencoder and Neighborhood Components Analysis (NCA) dimensionality reduction methods in Matlab and to observe the application of these algorithms on nine unlike datasets from UCI machine learning ... ...

    Abstract The central aim of this paper is to implement Deep Autoencoder and Neighborhood Components Analysis (NCA) dimensionality reduction methods in Matlab and to observe the application of these algorithms on nine unlike datasets from UCI machine learning repository. These datasets are CNAE9, Movement Libras, Pima Indians diabetes, Parkinsons, Knowledge, Segmentation, Seeds, Mammographic Masses, and Ionosphere. First of all, the dimension of these datasets has been reduced to fifty percent of their original dimension by selecting and extracting the most relevant and appropriate features or attributes using Deep Autoencoder and NCA dimensionality reduction techniques. Afterward, each dataset is classified applying K-Nearest Neighbors (KNN), Extended Nearest Neighbors (ENN) and Support Vector Machine (SVM) classification algorithms. All classification algorithms are developed in the Matlab environment. In each classification, the training test data ratio is always set to ninety percent: ten percent. Upon classification, variation between accuracies is observed and analyzed to find the degree of compatibility of each dimensionality reduction technique with each classifier and to evaluate each classifier performance on each dataset.

    Comment: To be published in the 2nd International Conference on Innovation in Engineering and Technology (ICIET)
    Keywords Computer Science - Machine Learning
    Subject code 006
    Publishing date 2019-12-11
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Book ; Online: Non-Intrusive Electrical Appliances Monitoring and Classification using K-Nearest Neighbors

    Khan, Mohammad Mahmudur Rahman / Siddique, Md. Abu Bakr / Sakib, Shadman

    2019  

    Abstract: Non-Intrusive Load Monitoring (NILM) is the method of detecting an individual device's energy signal from an aggregated energy consumption signature [1]. As existing energy meters provide very little to no information regarding the energy consumption of ... ...

    Abstract Non-Intrusive Load Monitoring (NILM) is the method of detecting an individual device's energy signal from an aggregated energy consumption signature [1]. As existing energy meters provide very little to no information regarding the energy consumption of individual appliances apart from the aggregated power rating, the spotting of individual appliances' energy usages by NILM will not only provide consumers the feedback of appliance-specific energy usage but also lead to the changes of their consumption behavior which facilitate energy conservation. B Neenan et al. [2] have demonstrated that direct individual appliance-specific energy usage signals lead to consumers' behavioral changes which improves energy efficiency by as much as 15%. Upon disaggregation of an energy signal, the signal needs to be classified according to the appropriate appliance. Hence, the goal of this paper is to disaggregate total energy consumption data to individual appliance signature and then classify appliance-specific energy loads using a prominent supervised classification method known as K-Nearest Neighbors (KNN). To perform this operation we have used a publicly accessible dataset of power signals from several houses known as the REDD dataset. Before applying KNN, data is preprocessed for each device. Then KNN is applied to check whether their energy consumption signature is separable or not. KNN is applied with K=5.

    Comment: To be published in the 2nd International Conference on Innovation in Engineering and Technology (ICIET)
    Keywords Computer Science - Machine Learning ; Electrical Engineering and Systems Science - Signal Processing
    Subject code 690
    Publishing date 2019-11-22
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Book ; Article ; Online: Deployment Strategies for Golden Rice in Bangladesh

    Rahman, Mohammad Chhiddikur / Rahaman, Md. Shajedur / Islam, Mohammad Ariful / Omar, Md. Imran / Siddique, Md. Abu Bakr

    A Study on Affordability and Varietal Choice with the Target Beneficiaries

    2021  

    Abstract: The sustainable development goals emphasized achieving food security and improved nutrition for all. As nothing is more important than health, all should have adequate basic nutrients to lead a healthy life. Vitamin A deficiency (VAD) is a major problem ... ...

    Abstract The sustainable development goals emphasized achieving food security and improved nutrition for all. As nothing is more important than health, all should have adequate basic nutrients to lead a healthy life. Vitamin A deficiency (VAD) is a major problem in large parts of the developing world. Apart from acute symptoms of eye problems, VAD also weakens the immune system, thus increasing the incidence and severity of infectious diseases. For adults, the implications can be serious too, especially for pregnant and lactating women. The most affected are the poor, whose diets are predominated by less nutritious staple foods on account of lacking purchasing power and limited awareness. ‘Golden Rice’ has been developed through genetic engineering at Swiss and German universities. It is a new type of rice that contains ‘beta-carotene’, which is converted into vitamin A inside the body as needed and gives the grain its golden color. It’s grown just like ordinary rice and aims to provide 30-50% of the estimated average requirement for vitamin A. It could improve the vitamin A status of deficient food consumers, especially women and children in developing countries. Some optimists praise it as the solution to overcome malnutrition and VAD. It already has received biosafety approval and released in the Philippines and hopes to release in Bangladesh soon. Prior to the release, the healthier rice team aimed to draw a deployment strategy of golden rice in Bangladesh. Therefore, this study has been conducted to assess the affordability and varietal choice of the targeted beneficiaries in the specific regions of Bangladesh.
    Keywords ddc:630 ; Golden rice ; Deployment policy ; Ex-ante ; vitamin A deficiency ; Bangladesh
    Subject code 360
    Language English
    Publisher Gazipur: Bangladesh Rice Research Institute
    Publishing country de
    Document type Book ; Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article ; Online: A Cost Efficiency Analysis of Boro Rice Production in Dinajpur District of Bangladesh

    Rahaman, Md Shajedur / Haque, Sadika / Sarkar, Md Abdur Rouf / Rahman, Mohammad Chhiddikur / Reza, Md Salim / Islam, Mohammad Ariful / Siddique, Md Abu Bakr

    2021  

    Abstract: The size of the farm is an important factor that reflects the efficient utilization of resources in farming. Therefore, this paper aims to investigate how the farm size affects the cost efficiency of rice production during the Boro season in Bangladesh. ... ...

    Abstract The size of the farm is an important factor that reflects the efficient utilization of resources in farming. Therefore, this paper aims to investigate how the farm size affects the cost efficiency of rice production during the Boro season in Bangladesh. In particular, the analysis aims to estimate the concentration of cost efficiency among the 240 small, medium, and large Boro rice growers sampled in the Dinajpur district. Descriptive statistics were used to evaluate the socioeconomic characteristics of rice farmers. A Cobb-Douglas type stochastic cost frontier model was employed to figure out how the rice farmers are cost-efficient. The sociodemographic factors that affect efficient investment in rice production also have been identified. The results of the study show a broad range of cost efficiency scores between 56.65 to 96.40% for the worse to the best rice-growing farmer, respectively with an average efficiency of 84.01%. The findings also show that the mean cost efficiency level of small, medium, and large farmers was 83.30, 85.58, and 94.43%, respectively. The land rental fees, human labor wages, irrigation prices, and pesticide prices are the key factors that contribute to the productivity of rice cultivation. The relatively higher level of cost efficiency among large farmers obviously demonstrates the notion that only large farmers in the study region are investing efficiently in rice growing. Irrespective of the farm size, the cost efficiency drivers found out that more efficient were the farmers who had more experience in farming, obtained training on rice production techniques, and better access to institutional credit. It is therefore recommended that rice farmers should be well trained, provided credit access along with developing rural set-up, and also provide extension services in order to increase the cost efficiency levels in Boro season.
    Keywords ddc:330 ; Cost effective ; Rice ; Farm size ; Stochastic frontier analysis
    Subject code 338
    Language English
    Publisher Mymensingh, Bangladesh: Farm to fork foundation
    Publishing country de
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article ; Online: Detection of COVID-19 Disease from Chest X-Ray Images: A Deep Transfer Learning Framework

    Sakib, Shadman / Siddique, Md. Abu Bakr / Khan, Mohammad Mahmudur Rahman / Yasmin, Nowrin / Aziz, Anas / Chowdhury, Madiha / Tasawar, Ihtyaz Kader

    medRxiv

    Abstract: The world economy as well as public health have been facing a devastating effect caused by the disease termed Coronavirus (COVID-19). A significant step of COVID-19 affected patient9s treatment is the faster and accurate detection of the disease which is ...

    Abstract The world economy as well as public health have been facing a devastating effect caused by the disease termed Coronavirus (COVID-19). A significant step of COVID-19 affected patient9s treatment is the faster and accurate detection of the disease which is the motivation of this study. In this paper, the implementation of a deep transfer learning-based framework using a pre-trained network (ResNet-50) for detecting COVID-19 from the chest X-rays was done. Our dataset consists of 2905 chest X-ray images of three categories: COVID-19 affected (219 cases), Viral Pneumonia affected (1345 cases), and Normal Chest X-rays (1341 cases). The implemented neural network demonstrates significant performance in classifying the cases with an overall accuracy of 96%. Most importantly, the model has shown significantly good performance over the current research-based methods in detecting the COVID-19 cases in the test dataset (Precision = 1.00, Recall = 1.00, F1-score = 1.00, and Specificity = 1.00). Therefore, our proposed approach can be adapted as a reliable method for faster and accurate COVID-19 affected case detection.
    Keywords covid19
    Language English
    Publishing date 2020-11-12
    Publisher Cold Spring Harbor Laboratory Press
    Document type Article ; Online
    DOI 10.1101/2020.11.08.20227819
    Database COVID19

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